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Make beautiful presentations, together

Stay in sync in your slides, with easy sharing and real-time editing. Use comments and assign action items to build your ideas together.

Present slideshows with confidence

With easy-to-use presenter view, speaker notes, and live captions, Slides makes presenting your ideas a breeze. You can even present to Google Meet video calls directly from Slides.

Seamlessly connect to your other Google apps

Slides is thoughtfully connected to other Google apps you love, saving you time. Embed charts from Google Sheets or reply to comments directly from Gmail. You can even search the web and Google Drive for relevant content and images directly from Slides.

Extend collaboration and intelligence to PowerPoint files

Easily edit Microsoft PowerPoint presentations online without converting them, and layer on Slides’ enhanced collaborative and assistive features like comments, action items, and Smart Compose.

Work on fresh content

With Slides, everyone’s working on the latest version of a presentation. And with edits automatically saved in version history, it’s easy to track or undo changes.

Make slides faster, with built-in intelligence

Assistive features like Smart Compose and autocorrect help you build slides faster with fewer errors.

Stay productive, even offline

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The best presentation software in 2024

These powerpoint alternatives go beyond the basics..

The latest presentation apps have made it easier than ever to format slides and create professional-looking slideshows without giving off a "this is a template" vibe. Even standard PowerPoint alternatives have updated key features to make it easier than ever to collaborate and create presentations quickly, so you can spend more time prepping for your actual presentation.

If, like me, you've used Google Slides unquestioningly for years, it's a whole new world out there. The newest crop of online presentation tools go way beyond the classic slideshow experience, with new features to keep your audience's attention, streamline the creation process with AI, and turn slide decks into videos and interactive conversations.

I've been testing these apps for the past few years, and this time, I spent several days tinkering with 25 of the top presentation software solutions out there to bring you the best of the best.

The best presentation software

Beautiful.ai for AI-powered design

Prezi for non-linear, conversational presentations

Powtoon for video presentations

Pitch for collaborating with your team on presentations

Gamma for conversational AI features

Mentimeter for audience engagement

Tome for generative AI features

What makes the best presentation app?

How we evaluate and test apps.

Our best apps roundups are written by humans who've spent much of their careers using, testing, and writing about software. Unless explicitly stated, we spend dozens of hours researching and testing apps, using each app as it's intended to be used and evaluating it against the criteria we set for the category. We're never paid for placement in our articles from any app or for links to any site—we value the trust readers put in us to offer authentic evaluations of the categories and apps we review. For more details on our process, read the full rundown of how we select apps to feature on the Zapier blog .

When looking for the best presentation apps, I wanted utility players. After all, slideshows are used for just about everything, from pitch decks and product launches to class lectures and church sermons. With that in mind, here's what I was looking for:

Pre-built templates. The best presentation tools should have attractive, professional-looking templates to build presentations in a hurry.

Sharing and collaboration options. Whether you plan to share your webinar slides later, or you just want to collaborate with a coworker on a presentation, it should be easy to share files and collaborate in real-time.

Flexibility and customization options. Templates are great, but top presentation apps should enable you to customize just about everything—giving you the flexibility to build exactly what you need.

Affordability. Creating compelling presentations is important, but you shouldn't have to bust your budget to make it happen. With capable free tools on the market, affordability is a top consideration.

Standalone functionality. There's no reason to use multiple tools when one can do it all, so I didn't test any apps that require and work on top of another presentation app like PowerPoint or Google Slides.

Familiar, deck-based interface. For our purposes here, I only tested software that uses slides, with the familiar deck-based editor you expect from a "presentation" tool (versus, for example, a video creation app).

While most apps now offer AI features in one way or another, it isn't a universal feature yet—and some apps' AI features leave a lot to be desired. For that reason, I opted not to make AI features a strict must-have for this year (though it probably will be a requirement next time I update the article). That means I've still included some apps that don't offer AI—if you opt for one of those, you can still easily get AI-generated images and text from a separate tool and copy them into your presentation app of choice.

Beyond that, I also looked for presentation apps that brought something unique to the table—features above and beyond what you can get for free from a legacy solution like PowerPoint or Google Slides.

Here's what my testing workflow looked like:

I went through any onboarding or guided tutorials.

I created a new deck, scanning through all available templates, noting how well-designed they were (and which were free versus paid).

I added new slides, deleted slides, edited text and images, and played around with other content types.

I changed presentation design settings, like color schemes and background images.

I reviewed and tested the sharing and collaboration options.

I tested out presenter view (when available).

After my first round of testing, I went back into the top performers to test any unique or niche features, like AI, brand settings, and interactive content. With that, these are the best presentation apps I found—each one really brings something different or radically easy to the table.

The best presentation software at a glance

The best free presentation software, canva (web, windows, mac, android, ios).

Canva pros:

Excellent free plan

Tons of amazing templates for all use cases

Feature-rich

Canva cons:

The AI tools aren't groundbreakingly useful

Canva offers one of the most robust free plans of all the presentation apps I tested. The app delays account creation until after you've created your first design, so you can get started building your presentation in seconds. Choose from an almost overwhelming number of beautiful templates (nearly all available for free), including those designed specifically for education or business use cases.

Anyone who's accidentally scrolled too far and been bumped to the next slide will appreciate Canva's editor interface, which eliminates that problem altogether with a smooth scroll that doesn't jump around. Choose from a handful of preset animations to add life to your presentations, or browse the library of audio and video files available to add. And Canva also has a number of options for sharing your presentation, including adding collaborators to your team, sharing directly to social media, and even via QR code.

Present directly from Canva, and let audience members submit their questions via Canva Live. Once you share a link to invite audience members to your presentation, they can send questions for you to answer. As the presenter, you'll see them pop up in your presenter view window, so you can keep the audience engaged and your presentation clear. Alternatively, record a presentation with a talking head bubble—you can even use an AI presenter here—to share remotely.

Canva has added a number of AI-powered tools , but I wasn't super impressed by them yet. The Magic Design tool, for example, isn't much more useful than the many pre-designed templates already available, while Magic Write is basically just white-labeled ChatGPT. These features will likely improve in time, but for now, you're better off starting from one of Canva's many great templates.

Canva pricing: Free plan available; paid plans start at $120/year for 1 user and include additional features like Brand Kit, premium templates and stock assets, and additional AI-powered design tools.

If you're looking for a capable free presentation tool with a more affordable upgrade, Zoho Show is worth a look. It's completely free for most features, offers a clean, intuitive editor, and includes a number of great templates. Plus, you can automate Zoho Show by connecting it with Zapier .

The best presentation app for AI-powered design

Beautiful.ai (web, mac, windows).

Beautiful.ai pros:

True AI design

No fussing around with alignment

Still allows for customization

Beautiful.ai cons:

No free plan

If you're like me, editing granular spacing issues is the absolute worst part of building a presentation. Beautiful.ai uses artificial intelligence to take a lot of the hassle and granular design requirements out of the presentation process, so you can focus on the content of a presentation without sacrificing professional design. If I needed to make presentations on a regular basis, this is the app I'd use.

Many apps have recently added AI design features, but Beautiful.ai has been doing it for years—and they've perfected the experience of AI design, ensuring the tool's reign as the most streamlined and user-friendly option for AI design.

The editor is a little different from most presentation apps, but it's still intuitive—and you'll start off with a quick two-minute tutorial. When creating a new slide, scroll through "inspiration slides" to find a layout you like; once you choose, the app will pull the layout and automatically adapt it to match the design of the rest of your presentation.

With 10 themes, several templated slides, over 40 fully-designed templates, and more than 20 different color palettes to choose from, Beautiful.ai strikes a perfect balance between automation and customization.

While Beautiful.ai doesn't offer a free plan, paid plans are reasonably priced and offer sharing and collaboration options that rival collab-focused apps like Google Slides. And speaking of Google, you can connect Beautiful.ai with Google Drive to save all your presentations there.

Note: I re-tested the generative AI feature (called DesignerBot) this year. It's great for adding individual slides to an existing presentation—automatically choosing the best layout and matching the design to the rest of the deck—but as with most other apps, it struggled to pull in relevant images.

Beautiful.ai pricing: Plans start at $12/month for unlimited slides, AI content generation, viewer analytics, and more. Upgrade to a Team plan for $40/user/month to get extra collaboration and workspace features and custom brand controls.

If you're a founder looking for an AI presentation tool for your pitch deck, Slidebean is a great Beautiful.ai alternative for startups. The app offers a number of templates; a unique, content-first outline editor; and a generative AI tool that builds a whole pitch deck based on your website. I didn't include it on the list mainly because of the price: the free plan is quite limited, and the paid all-access plan can only be billed annually.

The best presentation app for conversational presentations

Prezi (web, mac, windows, ios, android).

Prezi pros:

Doesn't restrict you to standard presentation structure

Lots of customization options

Prezi Video lets you display a presentation right over your webcam video

Prezi cons:

Steep learning curve

Struggling to squeeze information into a basic, linear presentation? Give Prezi a try. Unlike nearly all other presentation apps on the market, Prezi Present doesn't restrict the structure of your presentation to a straight line. The editor focuses on topics and subtopics and allows you to arrange them any way you want, so you can create a more conversational flow of information.

With the structural flexibility, you still get all the same customization features you expect from top presentation software, including fully-editable templates. There's a learning curve if you're unfamiliar with non-linear presentations, but templates offer a great jumping-off point, and Prezi's editor does a good job of making the process more approachable.

Plus, Prezi comes with two other apps: Prezi Design and Prezi Video. Prezi Video helps you take remote presentations to a new level. You can record a video where the presentation elements are displayed right over your webcam feed. Record and save the video to share later, or connect with your video conferencing tool of choice (including Zoom, Microsoft Teams, and Google Meet) to present live.

Prezi's generative AI feature works ok, but it's more useful as a wireframe. When I asked it to create a presentation about the Stanley Cup Playoffs, for example, the resulting content read a lot like a student writing a term paper in the broadest strokes possible to avoid doing any actual research.

Prezi pricing: Free 14-day trial and a free plan that includes up to 5 visual projects; paid plans start at $7/month and include additional features like private presentations and Prezi Present.

The best presentation app for video presentations

Powtoon (web, ios, android).

Powtoon pros:

Timing automatically changes based on the content on the slide

Can toggle between slideshow and video

Can orient presentation as horizontal, vertical, or square

Powtoon cons:

Limited free plan

Powtoon makes it easy to create engaging videos by orienting the editor around a slide deck. Editing a Powtoon feels just like editing a presentation, but by the time you finish, you have a professional video. 

You can edit your slides at any time, and when you hit play, a video plays through your deck—the feel is almost like an animated explainer video. Each slide includes the animations you choose and takes up as much time as is needed based on the content on the slide. Powtoon figures the timing automatically, and you can see in the bottom-right of the editor how much time is used on your current slide versus the total presentation. If you ever want to present as a slide deck, just toggle between Slideshow and Movie.

You'll likely need to subscribe to a paid plan to get the most out of Powtoon—like creating videos longer than three minutes, downloading them as MP4 files, and white-labeling your presentations—but doing so won't break the bank. Plus, you'll unlock tons of templates complete with animations and soundtracks.

One of my favorite Powtoon features is the ability to orient your video: you can choose horizontal orientation (like a normal presentation) or opt for vertical (for mobile) or square (for social media). When your presentation is ready, you can publish straight to YouTube, Wistia, Facebook Ads, and any number of other locations.

Powtoon pricing: Limited free plan available; paid plans start at $15/month and include white-labeling, priority support, additional storage, and more.

The best presentation app for collaborating with your team

Pitch (web, mac, windows, ios, android).

Pitch pros:

Google levels of collaboration

Assign slides to specific team members

Excellent generative AI feature

Pitch cons:

User interface is a little different than you're used to

Need to collaborate on presentations with your team? Pitch is a Google Slides alternative that gets the job done. As far as decks go, Pitch includes all the beautifully-designed templates, customizability, and ease of use you expect from a top-notch presentation tool. But the app really shines when you add your team.

The right-hand sidebar is all about project management and collaboration: you can set and update the status of your deck, assign entire presentations or individual slides to team members, plus comment or add notes. Save custom templates to make future presentations even easier and faster.

You can also invite collaborators from outside your company to work with you on individual decks. And if you opt for a paid plan, Pitch introduces workspace roles, shared private folders, and version history.

Pitch also offers one of the most impressive generative AI features on this list. It still struggles to pull in relevant images, but I found the AI-generated written content and design to be top-notch.

Pitch pricing: Free plan offers unlimited presentations, custom templates, and live video collaboration; paid plans start at $22/month for 2 users with additional workspace features, presentation analytics, and more.

The best presentation app for conversational AI

Gamma (web).

Gamma pros:

Creates fully fleshed-out presentations from a prompt

Conversational chatbot-like experience

Can still manually edit the presentation

Gamma cons:

Not as much granular customization

I tested a lot of apps claiming to use AI to up your presentation game, and Gamma's conversational AI features were head and shoulders above the crowd.

Simply give the app a topic—or upload an outline, notes, or any other document or article—approve the outline, and pick a theme. The app will take it from there and create a fully fleshed-out presentation. It's far from perfect, but Gamma produces a very useful jumping-off point. (Last year, it was by far the best, but this year, other apps are catching up.)

Here's the key: Gamma is much more geared toward the iterative, chatbot experience familiar to ChatGPT users. Click on the Edit with AI button at the top of the right-hand menu to open the chat, and you'll see suggested prompts—or you can type in your own requests for how Gamma should alter the presentation.

Once you've done all you can with prompts, simply close the chat box to manually add the finishing touches. While you do sacrifice some granular customizability in exchange for the AI features, you can still choose your visual theme, change slide layouts, format text, and add any images, videos, or even app and web content.

Gamma pricing: Free plan includes unlimited users, 400 AI deck credits, and basic view analytics; upgrade to the Plus plan for $8/user/month to get unlimited AI credits, remove Gamma branding, and more.

The best presentation app for audience engagement

Mentimeter (web).

Mentimeter pros:

Tons of audience engagement features

Simple for participants to interact

Mentimeter cons:

Less granular customizability

Bit of a learning curve

If you need to engage with an audience during your presentation, Mentimeter makes that easy. The app is designed around interactive elements like quizzes, surveys, Q&As, sliders, and more (even a Miro whiteboard!).

Each of these is included in a number of different, professional-looking templates, so you can build a fully interactive presentation super quickly.

When it's time to present, your audience members can scan the QR code with their phone cameras or type in the URL and access code to participate. There's one code per presentation (so they won't have to do this on every slide), which gives access to each slide as you move through the presentation.

There are two main drawbacks to this one, though. First, there's a bit of a learning curve and less familiar editing interface (but I found it pretty easy to learn with some practice). The other drawback is that you can't get as granular with the visual customization as you can with many other presentation tools.

Mentimeter pricing: Free plan includes 50 participants per month and 34 different slide types; upgrade to the Basic plan for $11.99/presenter/month to get unlimited participants and slide types, private presentations, and more.

The best presentation app for generative AI

Top-tier generative AI features

Simple, customizable templates

Intuitive doc-style editor

There's definitely a learning curve

Tome is one of the new additions to this list that I'm most excited about. If you're looking for generative AI that just genuinely works , it's definitely worth a look. The editor is a bit more stripped down than most presentation apps but intuitive nonetheless—it's almost a cross between your standard deck editor and a Notion-style doc.

To generate an AI deck, click Generate with AI in the top right, and either write your own prompt or choose from the example prompts that cover a handful of common use cases, like sales enablement and company pitches. Edit or approve the suggested outline, then generate the full presentation.

From there, you can edit each slide as a doc via the right-hand menu—without limits on how much information you can include. During the presentation, you simply size down any slides that take up more than the standard amount of space. It's super simple but somehow feels revolutionary in a presentation app.

Tome pricing: Free plan available for manual editing without AI; upgrade to the Professional plan for $16/user/month to use the generative AI and design tools, plus engagement analytics, branding tools, and more.

What about the old standbys?

You might notice a few major presentation players aren't on this list, including OGs Microsoft PowerPoint, Apple Keynote, and Google Slides. These apps are perfectly adequate for creating basic presentations, and they're free if you have a Windows or Mac device or a Google account.

I didn't include them on the list because the presentation space has really developed in the last several years, and after testing them out, I found these behemoths haven't kept pace. If they weren't made by Microsoft/Apple/Google, I might not even be mentioning them. They're pretty basic tools, they're behind the curve when it comes to templates (both quantity and design), and they don't offer any uniquely valuable features like robust team collaboration, branding controls, video, and so on.

Some of these companies (think: Microsoft and Google) are openly working on some pretty impressive-sounding AI features, but they haven't really been focused on their respective presentation solutions just yet. Rest assured, I'm watching this space, and the next time we update this article, I'll re-test tools like Copilot for PowerPoint and Gemini for Google Slides to see what new features may be available.

In any case, if you're reading this, you're probably looking for an alternative that allows you to move away from one of the big 3, and as the presentation platforms featured above show, there's a ton to gain—in terms of features, usability, and more—when you do.

What about PowerPoint and Google Slides add-ons?

While I focused my testing on tools with full feature-sets—those that can serve as your sole presentation tool—there are a ton of add-on tools you can use atop big name tools like PowerPoint and Google Slides.

If you're looking to expand what you can do with PowerPoint or Google Slides, apps like Marq (formerly Lucidpress), Plus AI , Slidesgo , and Simplified can help you do things like access additional templates, save branded assets, and generate AI presentations from the app you're already using.

Related reading:

Canva AI tools to improve your design workflow

The best online whiteboards for collaboration

How to share a presentation on Zoom without sharing your browser tabs and address bar

This post was originally published in October 2014 and has since been updated with contributions from Danny Schreiber, Matthew Guay, Andrew Kunesh, and Krystina Martinez. The most recent update was in April 2024.

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Kiera Abbamonte

Kiera’s a content writer who helps SaaS and eCommerce companies connect with customers and reach new audiences. Located in Boston, MA, she loves cinnamon coffee and a good baseball game. Catch up with her on Twitter @Kieraabbamonte.

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Best presentation software of 2024

Make perfect slides for speeches and talks

Best overall

Best for branding, best for marketing, best for themes, best for media, best unique.

• How we test

The best presentation software makes it simple and easy to create, manage, and share slideshow presentations.

1. Best overall 2. Best for branding 3. Best for marketing 4. Best for themes 5. Best for media 6. Best unique 7. FAQs 8. How we test

Presentation software runs at the heart of business sales, management, and development, so it's important to ensure you have the best presentation software for your needs. This is especially when looking to share ideas, concepts, and workflows, and the ability to present these in a simple and understandable way is essential.

However, while presentation software has been traditionally limited to text and images, it has widened its ability to work with different media such as video and audio . 

Therefore it's important for the best presentation software to not simply be easy and simple to use, but also be able to support additional media so that presentations can be more engaging, lively, and ultimately serve their purpose in educating and updating their intended audience.

Below we've listed the best presentation software currently on the market.

We've also listed the best free presentation software .

The best office software in the world is: Microsoft 365 There are many different office software suites out there, but Microsoft Office remains the original and best, offering an unsurpassed range of features and functionality that rivals just can't match.

Even better, Microsoft 365 - previously branded as Office 365 - is a cloud-based solution which means you can use it on any computer, mobile device, or smartphone, without having to worry about compatibility. All your files are saved in the cloud and synced between devices, so you can begin work on a document at home or in the office, then continue working on it on the go.

You can sign up to Microsoft 365 here .

The best presentation software of 2024 in full:

Why you can trust TechRadar We spend hours testing every product or service we review, so you can be sure you’re buying the best. Find out more about how we test.

1. PowerPoint

Our expert review:

Reasons to buy

For most people, Microsoft 's PowerPoint remains the original and best of all the presentation software platforms out there. While other companies have managed to catch up and offer rival products worthy of consideration, the fact is that PowerPoint's familiar interface and ubiquitous availability means it remains a favorite for the majority of people.

On the one hand, it's long been a staple of the hugely popular Microsoft Office suite, meaning that for most users this is going to be the first - and last - presentation software they are going to need to use.

Additionally, Microsoft has made PowerPoint, along with their other office products, available as free apps (with limited functionality) on both iOS and Android for mobile use, meaning it's even harder to avoid them. And this is before we even consider the inclusion of PowerPoint in Microsoft's cloud-based Microsoft 365.

It does everything necessary that you'd expect of presentation software, allowing you to add text and media to a series of slides, to accompany a talk and other presentations. There are easy-to-use templates included to help spice things up a little, but even a general user with little experience of it is likely to find themselves able to use PowerPoint without much trouble at all.

Overall, it's hard to go wrong with PowerPoint, and although Microsoft 365 has a nominal cost, the apps are free to use even if they do have more limited functionality.

Read our full Microsoft PowerPoint review .

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2. CustomShow

Reasons to avoid.

Branding says a lot about a business, and it’s something firms need to get right from day one – from a good logo to a suitable font. CustomShow is business presentation software that puts all these elements of branding first.

Using the system, you can design and present customized, branded presentations that reflect your company and the products you offer, featuring the aforementioned logo and custom fonts. As well as this, you get a slide library and analytics to ensure your presentations are a success.

What’s more, you can import presentations into the software, and use it to tweak them further. There’s also integration with SalesForce , and because the platform is cloud-based, you can access your presentations on computers, tablets, and smartphones. 

Considering the focus on branding, this offering could be good for marketing and sales teams, and it's used by major companies such as HBO and CBS Interactive.

3. ClearSlide

Just like CustomShow, ClearSlide has a niche focus for companies. The platform is targeted at firms looking to generate successful marketing campaigns, pushing sales via presentations (and more), not least through a range of analytics and metrics to work for sales and marketing.

With the product, you can upload a range of files, including PowerPoint, Keynote, PDF, and Excel. ClearSlide is integrated with other platforms, including Google Drive, Dropbox, and Salesforce.

This system is pretty complex and may offer too many irrelevant features for some businesses, but you can create customized content that reflects your company and the message you’re trying to get out to customers. There are also some good metrics and analysis features, and you can sign up for a free trial before making any decisions.

The real strength of ClearSlide comes from its focus on sales and marketing data, not least being able to track user engagement alongside other metrics.

4. Haiku Deck

Any presentation app will allow you to personalize your slides to at least some extent, but Haiku Deck goes one step further than the competition. It comes with a wide range of themes suited to different needs, and you also get access to 40 million free images from the Creative Commons collection.

When it comes to creating a presentation, you have the option to do so on the web, which means your presentation is accessible across a range of mobile devices as well as desktops. Regardless of the device used, you’re able to select from a variety of different fonts, layouts, and filters to make the perfect presentation.

The great thing about these various customization options is that they’re categorized into different industries and use cases. For instance, you’ll find themes for teaching, cooking, real estate, and startups. Most of the features require you to be online, but hopefully, you’ll have a sturdy net connection wherever you go.

5. SlideDog

It’s all too easy to end up creating a presentation that’s unappealing, and the last thing you want to do is make the audience fall asleep. SlideDog lets you combine almost any type of media to create a rich presentation that’s sure to keep the viewers’ peepers open, avoiding the ‘cookie cutter’ look that makes presentations seem dull.

Marketed as a web-based multimedia presentation tool, it gives you the ability to combine PowerPoint presentations, graphics, PDF files, Prezi presentations, web pages, pictures, videos, and movie clips. You can drag these into custom playlists and display them to your audience with ease.

You’re able to remotely control your presentations and playlists from your smartphone, the web, or a secondary computer, and there’s also the option to share slides in real-time. Audience members can even view your slide from their own devices by clicking a link. That’s a handy feature if you’re looking to create an immersive presentation experience.

SlideDog is probably the cheapest of the presentation software featured, with a free account that will cover the essential features. However, for live sharing and premium support, you need to upgrade.

Read our full SlideDog review .

Prezi is one of the more unique presentation tools. Instead of presenting your graphics and text in a slide-to-slide format, you can create highly visual and interactive presentation canvases with the goal of “emphasizing the relationship between the ideas”.

Presentations can also be tailored to the specific audience, as this is a flexible platform that’s capable of skipping ahead, or veering off into a side topic, without having to flip through all the slides to get to a particular bit.

For business users, there are a variety of handy tools available. By downloading Prezi , you can build and edit presentations with your colleagues in real-time, which is perfect for companies with teams based around the globe.

When you have created a presentation you’re happy with, you can present it live (in HD) and send a direct link to viewers. There are some analysis tools here, too – you can see who’s accessed your presentation, which parts of it, and for how long. The app is available for Mac and Windows devices.

Read our full Prezi review .

Other presentation software to consider

Google Slides  is part of the Google Workspace (formerly G Suite) office platform intended as an online alternative to Microsoft Office. It may seem a little limited by comparison to PowerPoint, but as it's browser-based that means cross-platform compatibility. Additionally, it allows for collaborative work, and Google Slides really works well here. On top of the fact that it integrates with the rest of the Google Workspace apps, not least Google Drive, and you have a contender. 

Zoho Show  is another of the many, many tools and apps that Zoho has made available for business use. It also operates in the cloud so it's accessible to any device with a browser, and it also allows for collaborative work. You can also easily share the link for users to download, or provide a live presentation online. The updated version has a simpler and easier to use interface and comes with a free version and a paid-for one with expanded features.

Evernote  is normally thought of as just note-taking software, but it does provide the option to create a presentation you can share online or with an audience. In that regard, it's a little more limited than the other options in not being dedicated presentation software. However, as an easy and handy way to pull together a presentation quickly, it could serve as a backup or last-minute option, especially if Evernote is already being commonly used by you.

LibreOffice Impress  is part of the open-source suite offered as a free alternative to Microsoft Office, and comes with a powerful array of tools and editing options for your presentation, not least working with 3D images. It's supported by a large community, so it's easy to find an array of additional templates. If there is a limitation it's that it's software you download and install rather than web-based, but any presentations created should be easily portable to the web if needed.

Adobe Spark  does things a bit differently, as rather than just use images it's geared toward video as well. This makes for potentially more powerful multimedia presentations, especially as Adobe also has a big selection of photos and images available for its users. There is a free tier for core features but requires a subscription for custom branding, personalized themes, and support.

Slides  comes with a lot of features in an easy-to-use interface, and involves setting up presentations using drag and drop into an existing grid. It's also internet-based so there's no software to download, and it only requires a browser to use and access. 

Presentation software FAQs

Which presentation software is best for you.

When deciding which presentation software to download and use, first consider what your actual needs are, as sometimes free platforms may only provide basic options, so if you need to use advanced tools you may find a paid platform is much more worthwhile. Additionally, free and budget software options can sometimes prove limited when it comes to the variety of tools available, while higher-end software can really cater for every need, so do ensure you have a good idea of which features you think you may require for your presentation needs.

How we tested the best presentation software

To test for the best presentation software we first set up an account with the relevant software platform, whether as a download or as an online service. We then tested the service to see how the software could be used for different purposes and in different situations. The aim was to push each software platform to see how useful its basic tools were and also how easy it was to get to grips with any more advanced tools.

Read how we test, rate, and review products on TechRadar .

We've also featured the best alternatives to Microsoft Office .

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Nicholas Fearn is a freelance technology journalist and copywriter from the Welsh valleys. His work has appeared in publications such as the FT, the Independent, the Daily Telegraph, The Next Web, T3, Android Central, Computer Weekly, and many others. He also happens to be a diehard Mariah Carey fan!

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It’s you and your presentation together on screen — the easiest way to drive engagement in your hybrid and virtual meetings. Use your web camera to put yourself in the background of your presentation.

Present live over video conference

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A head start for any project

Jump-start your presentations with professionally designed templates. From building a pitch deck to crafting a lesson or telling a visual story, we have you covered.

There's even more to Prezi

Prezi has the features you expect and more to make your next presentation your best.

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Turn your slides into engaging presentations.

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The 11 Best Presentation Software to Use in 2023

The ability to effectively share ideas, illustrate a concept, and convince an audience is invaluable whether you’re a student or a C-level executive. These days, the presentation software you use to create presentations is just as important as your public-speaking skills.

On top of that, most companies have transitioned to remote work recently due to the current coronavirus situation, and now need to share their stories online through a virtual conference room with their distributed teams and external audience members.

That’s why we’ve come up with a list of some of the best presentation software available right now, so you can choose a compatible and innovative presentation maker that includes the best presentation tools to suit your specific needs.

Choose the best presentation software by weighing the pros and cons

You’ll see some of the most popular presentation apps: from free to paid subscription platforms, and slideshow applications to full-blown visual design presentation software with interactive features and more.

Each presentation software has its pros and cons, so it’s up to you to figure out which suits your needs best; consider the software learning curve, whether your company is made up of Mac users or Windows users and the software compatibility, if you need an enterprise account or free account, etc.

Let’s dive in!

1. Piktochart

Piktochart is a presentation software that can create a variety of design visuals, from infographics to social media stories.

An area in which Piktochart shines is crafting unique presentations. 

On Piktochart, users can choose from a wide range of professionally-designed presentation templates .

These custom templates include everything from monthly marketing reports to employee onboarding templates.

This broad selection of customizable templates is especially useful for those who don’t have much design experience or know-how but need to create a visually stunning unique presentation in a pinch. 

Piktochart’s presentation maker also makes it easy to edit presentations and include design elements such as lists, timelines, comparisons, graphs, and different types of charts through drag-and-drop tools.

You can even make visual maps and interactive charts to keep your audience engaged throughout your presentation. 

And if your company uses a Piktochart TEAM plan , you can enjoy the platform’s ability to store brand assets , color schemes, and bespoke templates. Here, replicating company-branded visuals is a breeze. 

Piktochart comes with a free version but with certain limitations. Active visuals are capped at five per month and published visuals have a Piktochart watermark.

If you want features such as team template collaboration, project sharing, and annotated commenting, you’ll have to get a Team account. To sum it up:

• Lots of professionally-designed templates 
• Good for both design professionals and non-professionals 
• Easy to store brand assets and bespoke templates for future presentations
• Access presentation tools from anywhere via a web browser
• Free presentation app version available
• Might take some getting used to if you’re used to PowerPoint presentations

2. Microsoft PowerPoint

Microsoft PowerPoint is often the first presentation software that comes to mind.

Once considered the “O.G.” and best presentation software available, it is still widely used and has a familiar interface—which means most users are comfortable with it. 

This presentation app has everything you need to create a presentation: from animated transitions for interactive presentations to pre-installed fonts and graphic elements.

Users can also upload their own fonts, graphics, and images into their finished presentation.

Lastly, it’s available as part of the Microsoft Office software package; and you can work on your presentations via the web and mobile devices, for offline viewing as well as online. 

However, PowerPoint is no longer considered the best presentation software, as it has very few templates to choose from, and these tend to fall quite flat compared to modern apps and software.

It’s easy to fall back into boring slideshow PowerPoint files if you don’t know what you’re doing.

And because most people use PowerPoint, you’re likely using the same template as the next guy. 

As standalone presentation software, PowerPoint is pricey at US$139.99—and accessible through only one device unless you upgrade your package.

And while PowerPoint is primarily a slideshow application and presentation maker, its strengths are limited to this category. 

So if you’re looking for the best presentation software, and bang for your buck for a robust presentation tool, you might want to look elsewhere. 

• Market leader in slideshow applications to create slides
• Widely used and familiar interface for the presentation process
• Reliable and usable on most devices as well as being a desktop app
• Flat templates
• Limitations with its standalone-presentation software price

3. Google Slides

Google Slides is a slideshow application that is very similar to PowerPoint.  But there are three main differences: it’s fully online (while also allowing for offline viewing), collaborative, and free. 

The great thing about Google Slides (besides the fact that it’s completely free for anyone with a Google account) is that you can log on via your browser or through its official app. 

You can access all Google Slides presentations from any device (mobile, tablet, and desktop), and share them with other people so you can collaborate in real-time. 

Google Drive allows all your presentations to live on the cloud, accessible to all marketing and sales teams, with unparalleled ease of use.

And there’s no need to worry about disruptions as all changes are saved as they happen, as long as you have an active internet connection. 

Additionally, anyone familiar with PowerPoint will be comfortable using Google’s iteration and likely be delighted by Google Drive and the slide library available. 

It’s also a lot simpler, so even those new to presentation-making will find it easy to navigate. 

However, some might find Google Slides too simple as it lacks the wealth of features available on PowerPoint. 

These include embedding videos from sources other than YouTube, plus adding audio tracks and sound effects, limiting the ability to create unique interactive presentations. 

Some users also encounter issues with downloading and exporting to different formats, including PowerPoint. 

Some slides may even turn out completely different from the original version. 

All in all, Google Slides is a great option for those who are looking for a free application and only need to create simple presentations. 

• The free plan supports professional presentations
• Web-based and collaborative to create presentations
• Simple and familiar interface for an online presentation software
• Too simple for advanced presentation making
• Difficult to export to other formats
• Limited templates and customization options for interactive content

You could say Keynote is Apple’s version of PowerPoint. It’s also a slideshow application—but in typical Apple fashion, it comes with a sleek, minimalist interface and is considered one of the best presentation apps on the market. 

There are 30 different themes to choose from, which serve as templates for those who need a quick fix. And it can do most of what PowerPoint can. 

Keynote’s main perk is that it’s part of the Apple ecosystem. 

That means it has built-in iCloud and Apple Watch support so users can control their presentation from their mobile device or even their wrists with just a click. 

This presentation app comes pre-installed on most Mac devices. Otherwise, you can purchase it from the Apple store for just US$9.99 for mobile and US$19.99 for OS X. 

The big downside is that Keynote is exclusive to Mac OS. 

Non-Apple users can create, upload, and sync their own Keynote presentations through their iCloud Drive, but this presentation app is only truly helpful only for those who use multiple Apple devices. 

And if you’re used to working on PowerPoint, you might find Keynote a bit confusing in the beginning, especially when editing presentations. 

• Sleek, minimalist interface 
• Free with most Apple devices
• No access for PC and Android devices except through iCloud

5. SlideDog

Sliding away from straightforward slideshow applications and other presentation apps, SlideDog is a web-based multimedia presentation tool that lets users combine different types of media to create and edit presentations. 

This includes everything from PowerPoint decks to videos and even PDFs that can all be played side by side without any awkward transitions. 

It’s also extremely easy to customize a SlideDog presentation. 

You just need to upload the files into the SlideDog web browser application, and then drag and drop them according to the order in which you want them to play. 

You can control your presentations and playlists from another device, and audience members can view your slideshow on their devices by clicking a link. 

SlideDog has a free presentation app version that provides all of the basic features. 

However, live sharing and premium support are only available with a Pro account that costs US$99 per year, and not via the free version alone.

While SlideDog is technically considered presentation software, you can’t actually create presentations on it. 

You can simply stitch together different pre-made presentations in various formats into what is essentially a playlist. 

Lastly, SlideDog supports only Windows devices, so Apple and Linux users can’t use it. 

• Supports a lot of different media
• Provides live-sharing
• More dynamic compared to the usual slideshow presentation
• Only collates media; doesn’t create them

6. Haiku Deck 

Ever come across presentations with size-eight fonts and blocks of indecipherable paragraphs on each slide? 

You can avoid such an unfortunate scenario with Haiku Deck. 

HaikuDeck is a web and mobile application that favors images over text. 

It works by limiting the number of words users can put on each slide, and allowing them to search for images on their platform related to the slide’s main idea. 

This makes it ideal for those who want to simplify their thoughts and let the images do all the talking. 

Users have over 40 million royalty-free photos to choose from, plus dozens of simple slide layouts on the platform itself. 

While this certainly simplifies the process of creating a visually rich presentation, it can be limiting for those who need to include more information into their slides. 

It’s a great option for someone giving a TED Talk, for example.

But for others who need to pass on more facts and figures, having a built-in word limit might be too restrictive.  

• Simple and easy to use 
• Access to millions of royalty-free stock images
• May be too simple for some
• No Android support
• Limited features

7. Prezi Business

Among the other presentation software on this list, Prezi Business might be one of the most unique presentation tools. 

Rather than offering a regular slideshow format, Prezi looks more like a 3D interactive mind map where viewers jump dynamically from one idea to the next. 

You can zoom in on one “slide” and then zoom out for the next. 

Prezi has over 100 templates to choose from and comes with a very simple interface and a drag-and-drop style of editing. 

It’s compatible with both Mac and PC desktops as well as smartphones. 

It’s also similar to a regular PowerPoint deck in that you can jump back and forth from one “slide” to the next. 

And like SlideDog, you can send viewers the link to the presentation as you’re presenting. 

Also, up to 10 people can work on a Prezi presentation at the same time, one of its main selling points. 

This is great for collaboration, but having so many hands-on deck at once can get messy. 

• Dynamic and immersive presentations
• Highly visual
• Easy to use
• May not be appropriate for all types of presentations

In a world of slides and presentations, standing out is the key. Ludus brings the flair of graphic design into the world of presentations.

At its core, Ludus is the bridge between presentation tools and design software. It enables users to infuse their slides with the kind of design elements you’d typically find in advanced design platforms.

Not only can you import assets from design giants like Adobe, but its seamless integration with tools like Unsplash and Giphy makes sourcing visuals a breeze.

It’s a fairly affordable tool for all its features compared to the other paid options in this list, as users pay 12.49 euros monthly (if billed annually).

However, while Ludus’ robust design capabilities can elevate the look of your presentation, those unfamiliar with design tools might find there’s a learning curve.

• Merges presentation creation with advanced design tools.
• Seamless integration with popular design platforms and visual databases.
• Offers a unique edge in presentation aesthetics.
• Might be a tad overwhelming for non-designers
• Can have a steeper learning curve for those used to more straightforward platforms

9. Slidebean

Crafting a compelling presentation demands not only compelling content but also a design that can captivate your audience. Enter Slidebean.

Slidebean offers an intelligent design solution, using AI to transform raw content into professionally styled presentations. This platform streamlines the design process, allowing you to focus on the message rather than fretting over aesthetics.

The basic plan is free and allows you to create a presentation. But if you want to share or download your presentations, as well as unlock the full suite of features, you’ll need to sign up for the All-Access plan priced at $199 per year.

While it provides a quick and efficient method to produce polished slides, it also offers features for sharing, collaboration, and viewer analytics, adding an edge to your presentation strategy.

However, for professionals who prioritize granular design control, the automated design might feel limiting at times.

• AI-driven design ensures visually appealing presentations.
• Features for collaboration and viewer insights.
• Efficient design process reduces time and effort.
• Might not offer the detailed design customization some users desire.
• Automated choices may not always align with specific branding or style preferences.

10. ClearSlide

Having great visuals to drive your point home can be the difference between getting a sale across the line or customers walking away. ClearSlide stands out in this area as a presentation tool for businesses laser-focused on boosting their sales and marketing game.

At its core, ClearSlide is all about leveling up business presentations. Whether you’re marketing a new product or tracking client engagement, it’s got tools that cater to every need.

Whether it’s a PowerPoint, a PDF, or something from Google Drive or Dropbox, ClearSlide makes it simple to upload and work with these files.

The unique edge? ClearSlide’s virtual meeting space pops open with just a click. It’s all about seamless, professional presentations without the hassle.

Beyond just slides, the platform dives deep into metrics and analytics, ensuring every presentation is backed by data-driven insights. And the tool is available for $35 per month, which isn’t too pricey for medium-sized businesses.

However, its complexity isn’t for everyone. For some, the variety of features might seem a tad overwhelming, and its focus on metrics might be a bit much for those just wanting a basic presentation tool.

• Seamless virtual meetings and presentations
• Integrates with popular platforms
• Offers insightful analytics for sales and marketing
• Might feel complex for some users
• Limited transition and design effects
• Mobile experience could be better

Stepping into the world of animation, Vyond, once known as GoAnimate, allows users to turn their narratives into professional animated videos. For those looking to elevate their content without diving deep into animation complexities, Vyond can be the go-to tool.

This platform is more than just drag-and-drop animations. It integrates AI capabilities with Vyond Go, which transforms text prompts into rough-cut videos.

Fancy a quick draft for your upcoming project? This AI assistant is up for the task. And if perfection is your game, take it to Vyond Studio, filled with an array of characters, templates, and backgrounds.

The Essential Plan at $25 per month is suitable for individuals on a budget. However, if you want to export videos at 1080p and above, have collaboration tools, or different export options, you’ll need to sign up for the Professional Plan at $92 per month.

As robust as the tool is, there are still some kinks to iron out. AI voiceovers might still need some tweaks, and detailed color customizations can be a bit tricky, but the tool’s strengths, especially for businesses, are undeniable.

• Hassle-free video creation for beginners to experts
• Generous library of pre-made assets
• AI-powered video and script creation with Vyond Go
• AI voiceovers might feel a bit robotic
• Some customization limitations for specific props and scenes

The best presentation software is…

 …completely up to you! 

When it comes to presentation software, the world is your oyster. 

Each of these tools either has a free or trial version for you to check out, so you don’t have to commit just yet. 

When it’s time to choose, consider the following aspects to find the right presentation software for you: 

• Ease of use. Is it easy for you to understand or will it require lots of training before you can start creating presentations? 
• Accessibility. Can you access your presentation software from any device or are you limited to carrying your laptop to every presentation? 
• Real-time collaboration. Can multiple people work on the same project or do you have to keep downloading and emailing drafts? 
• Create design tools. Can you create presentations with dynamic design elements or are you stuck with the same kind of slide each time? 
• Template availability. Is this tool only accessible to a design professional or can anyone create stunning presentations through pre-designed and updated templates? 

Piktochart , for example, would be a fantastic presentation software choice among the long list of PowerPoint alternatives for teams looking for a variety of eye-catching designs without requiring much technical know-how. Meanwhile, Microsoft PowerPoint might be the best presentation software for those who are just looking to play it safe. 

Hopefully, this best presentation software list sheds some light on the tools at your disposal. Choose wisely! 

Other Posts

How to Make a Presentation (2023 Guide With Tips & Templates)

How to Nail Your Brand Presentation: Examples and Pro Tips

Presentation Design: A Step-by-Step Guide

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AI presentation maker

When lack of inspiration or time constraints are something you’re worried about, it’s a good idea to seek help. Slidesgo comes to the rescue with its latest functionality—the AI presentation maker! With a few clicks, you’ll have wonderful slideshows that suit your own needs . And it’s totally free!

Generate presentations in minutes

We humans make the world move, but we need to sleep, rest and so on. What if there were someone available 24/7 for you? It’s time to get out of your comfort zone and ask the AI presentation maker to give you a hand. The possibilities are endless : you choose the topic, the tone and the style, and the AI will do the rest. Now we’re talking!

Customize your AI-generated presentation online

Alright, your robotic pal has generated a presentation for you. But, for the time being, AIs can’t read minds, so it’s likely that you’ll want to modify the slides. Please do! We didn’t forget about those time constraints you’re facing, so thanks to the editing tools provided by one of our sister projects —shoutouts to Wepik — you can make changes on the fly without resorting to other programs or software. Add text, choose your own colors, rearrange elements, it’s up to you! Oh, and since we are a big family, you’ll be able to access many resources from big names, that is, Freepik and Flaticon . That means having a lot of images and icons at your disposal!

How does it work?

Think of your topic.

First things first, you’ll be talking about something in particular, right? A business meeting, a new medical breakthrough, the weather, your favorite songs, a basketball game, a pink elephant you saw last Sunday—you name it. Just type it out and let the AI know what the topic is.

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They say that variety is the spice of life. That’s why we let you choose between different design styles, including doodle, simple, abstract, geometric, and elegant . What about the tone? Several of them: fun, creative, casual, professional, and formal. Each one will give you something unique, so which way of impressing your audience will it be this time? Mix and match!

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InfoQ Homepage Presentations Building Guardrails for Enterprise AI Applications W/ LLMs

Building Guardrails for Enterprise AI Applications W/ LLMs

Shreya Rajpal introduces Guardrails AI, an open-source platform designed to mitigate risks and enhance the safety and efficiency of LLMs.

Shreya Rajpal is the creator and maintainer of Guardrails AI. Most recently, she was the founding engineer at Predibase, where she led the ML infrastructure team. In earlier roles, she was part of the cross-functional ML team within Apple's Special Projects Group and developed computer vision models for autonomous driving perception systems at Drive.ai.

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Rajpal: I'm going to be talking about guardrails for enterprise applications and breaking down the problem of reliability, safety, consistency for large language models. My name is Shreya. I am currently the CEO and co-founder of Guardrails AI. I've spent about a decade in machine learning. I started out doing research and publishing papers of top-end use in classical AI, deep learning. I then spent a number of years in self-driving cars working in everything in machine learning and self-driving cars, including sensors, perception, decision making. I really enjoyed that job, but then left to join an MLOps company based here in the Bay Area as their first hire, and led the ML infra team while I was there. I started Guardrails AI, which is a company I now lead.

AI Evolution

This is why we care about this, in the last year or so we've seen a Cambrian explosion of applications in AI. There are a lot of really interesting applications that we see, Auto-GPT and completely fully agentic workflows are something that we've seen. In addition to that, we've also seen very specific applications in high value use cases, so mental illness, medicine, law, sales. This is one of my favorite tweets that captures some of the early sentiment around what LLMs and AI will do for us, which is like, software engineering as we know it is dead, AI can code better than us. We can see interest in AI over time, really expanding. If you look at the timeline here, somewhere around this point is the ChatGPT release, and immediately interest in AI skyrocketed. Suddenly, everybody who was not related to software, like my mom was talking about artificial intelligence, and for the first time got what work I did. It's really nice to see everybody's imaginations sparked with what we'll be able to build with AI systems.

The reality is a little bit different. As you work with AI applications, you come across many issues with them that lead to, at the end of the day, reduced value from what you're able to get out of using AI. I think this was a case that got a lot of attention, because this was a very real-world implication of somebody overly relying on AI systems when they aren't at a place where they're very reliable. A lawyer used ChatGPT in court, and cited fake cases, and is commonly now known as the ChatGPT lawyer. I think the quality of answers on Stack Overflow, for example, has gone down, a bunch of other use cases. I've quoted some figures from an article which was published by Sequoia Capital, about the state of the world in generative AI. If you look at path to 100 million users, ChatGPT is one of the fastest climbing applications that got to 100 million users. Then if you actually look at like one month retention, or daily active users of those applications, generative AI applications tend to have much fewer retention. I haven't included the DAU graph here, but go check it out at this link, https://www.sequoiacap.com/article/generative-ai-act-two/. Much fewer compared to traditional applications that have also seen that meteoric rise. This is a very interesting trend. It points to the case that right now out of generative AI, people aren't getting the value that you really need to be able to get from other software systems that you used to.

Why is this the case? A common symptom that we see with people is like, my application completely worked in prototyping, it never failed. The moment I handed it off to someone else, it started erroring out, or my API stopped working. I think the root cause of the symptom is that machine learning is fundamentally non-deterministic. This is not a bug but a feature. If machine learning was fully deterministic, it wouldn't have the creativity that all of us have come to rely on for ChatGPT, other generative AI use cases. It wouldn't adapt so well or it wouldn't be as flexible as it is for a lot of applications. A lot of the goal here is like, how do we work with something that is still really useful but works differently than traditional software that we've been used to. I've outlined that problem here, which is that any software API, when you build with it, that software API is going to be deterministic. Maybe that service might be down sometimes, but any query that you send it, or any response that you send it is going to give you the same output response, time after time. As a result of this property, we're able to build really complex software systems that chain together multiple APIs, and all depend on each other.

Machine learning is different. Even if availability or uptime might be high for machine learning models, fundamentally, you're going to end up getting different responses from a machine learning model every single time you use it. For example, let's say you are building a generative AI application. You read all of the best guides on how to do prompt engineering, you end up constructing the best prompt for yourself. You're like, ok, I have this problem solved, now this prompt works. Every time you run that prompt in production, even without changing anything, you're going to end up seeing different responses. It doesn't mean that the ChatGPT or OpenAPI API is down, it just means that the model fundamentally responds to the same input differently. As a result, a lot of the applications that people are building using generative AI still are marrying traditional software systems. You chain together multiple APIs, you try to get the LLM to reason and that output is sent to another LLM, chaining is a very common abstraction that has gotten a lot of popularity. This fundamentally breaks down and doesn't work the moment you factor into account how common it is for AI model APIs to break down or just generate outputs that are not in line with what you expect them to be or what you tested them out to be.

Getting 'Correct' Outputs Always is Hard

Summarizing the problem, is that getting correct outputs always is hard. This is a fundamental assumption that we take for granted with traditional software systems. Some common issues of incorrectness that you end up seeing in applications are hallucinations, falsehoods, lack of correct structure. For example, a lot of people use ML APIs to generate JSON payloads that then you can use to query a tool or something. Those JSON payloads are often structured in weird ways. This is all on the output side. Even on the input side, you might be susceptible to prompt injection attacks, which is essentially a framework where someone can cause you to reveal your prompt, which might be your secret sauce, something that you don't actually want to reveal. By, in a very oversimplified way, like asking the LLM nicely, but often requires a lot of hacking around the limits of what the LLM response will do and won't do. You basically end up seeing the developer's prompt which you can use maliciously. To further complicate the problem, the only tool that is available to any developer that is working with a generative AI system is the prompt. This is a very novel world where you can't really code your way out of these problems, you often have to once again, write a bunch of English and figure out how different models respond to the same English prompt.

All of this combines together into a situation where the use of LLMs is limited, wherever correctness is critical. For example, one of the most common, most successful applications of LLMs that we see is GitHub Copilot. I use it in my workflows every day. Most of the folks at my company use it. If you look at like, what makes it tick, it's that the LLM output is generated, and if it's wrong, you just brush it off, move on. Only if it's correct do you accept that output, and then actually incorporate it. This has limited value. This makes it so that you can't really use LLMs in healthcare applications, or in banking, or corporate or enterprise applications where there's a substantial cost to the LLM being incorrect. This is a problem that I personally find very fascinating. Not just because it's like, you have this fascinating technology on your hands, how do you make it work with how we build software today? Also, because this is a pattern that we see a lot in self-driving cars, for example, where ML models are again stochastic at the end of the day, but they need to work in a real-world scenario where there's a substantial cost to that ML model being wrong.

Guardrails AI (Safety Firewall Around LLMs)

The problem here that I'm focused on, and Guardrails AI the open source is focused on, is, how do we add correctness guarantees to LLMs? This is a screenshot of the open source. This is the entire problem that the open source focuses on. I'm going to dig deeper into how architecture, we situate ourselves into a part of the stack that makes it so that we're able to assess these problems. Guardrails AI acts as a safety firewall around your LLMs. This is a high-level system overview. If you look at how traditional AI applications are built, you look at, there's some AI application. Within that AI application, you have a component, which is your LLM logic. That LLM logic, basically, there's an input that is a prompt. That prompt gets sent to your LLM API. That LLM API returns an output, and that output flows back into your application. How Guardrails AI is different is that we essentially advocate for using a verification suite, in addition to any LLM API that you use. This sits something like a sidecar in addition to your LLM API, and is essentially acting as a safety firewall around your LLM, and making sure that all of the functional risks and guarantees that you care about are all accounted for. Now, instead of sending your output back directly to your application logic, you would first pass that output to your verification suite. This verification suite would have a bunch of independently implemented guardrails. These guardrails all look for different risk areas, different safety concerns within your system. Let's say one of these safety concerns could be making sure that there is no personally identifying information in your output. If you don't want to send any PII that is accidentally generated by the LLM to your user, this can be something that filters that. Making sure that there is no profanity in your system, especially if this is a customer facing application.

Let's say you're also building a commercial application. For example, you are McDonald's, and you want to build a chatbot for taking orders via chat, even if a customer asks you about Burger King, you never actually want to be talking about Burger King. It also extends to non-English output. For example, let's say you're generating code with an LLM, there's a lot of code generation applications that large language models are really good at. You might want to make sure that any code that is generated is executable and correct for your development environment. Finally, let's say you're using LLMs to summarize an email, or summarize really long internal design documents, so you can make sure that the summary is actually grounded in the reality of what that document is, and is correct, and is not hallucinating any outputs. Now your workflow looks like, instead of passing that output directly to your end user, the verification suite independently passes and fails. You can set policies on how much you care about each single guardrail. Maybe mentioning competitors is not as bad for you, but profanity is really bad. That is one of those things that you can configure and make sure that is handled appropriately. Only if you pass validation do you send this out into your application logic. If you fail validation, Guardrails uses a very unique and novel capability off large language models, which is their ability to self-heal. Essentially, if you provide them enough context about why they're wrong, more often than not, LLMs have the ability to correct themselves and incorporate your feedback. In this case, Guardrails will automatically construct a new prompt for you that talks to the LLM in a way so that it's more likely to correct itself. This whole loop carries over again until you pass verification or until your policies give out and you are out of budget.

Why Not Use Prompt Engineering or a Better Model?

Why not use prompt engineering or a better model? First of all, an alternative to using this safety firewall is directly trying to control the LLM output with a prompt. As we discussed before, LLMs are sarcastic. The same input, even if you have the best prompt available to you does not guarantee the same output. Finally, prompts don't offer any guarantees, because LLMs don't always follow instructions. Anecdotally, a lot of people that I talk to and work with, they go to these extremes of like, ok, something really bad will happen, like a man will die if you don't give your output in this certain fashion, which is not a very sustainable way of building good, reliable software. I think the alternative is controlling the LLM output with a better model directly. The first is if you want to fine-tune a model, and this allows you to really build a better foundation for your application. The issue still remains that it's expensive and time consuming to train and fine-tune a model on any custom data. My entire background in machine learning, I've spent on training or fine-tuning models or building infrastructure to train and fine-tune models. A lot of the explosion that we're seeing with applications is because of how easy it is to now not have to do that, not have to build a dataset that works for your problem, not have to manage the training infrastructure, but instead just call an API and get the right output. Doing this, to some degree, limits the usability of generative AI applications. Alternatively, if you're using commercial APIs, so for example, you rely on OpenAI GPT-4, like newer GPT releases, I think the issue there ends up being that you as a developer don't have any control over model version updates. As someone who works with these models every day, one of the things that happens is that even without official model version releases, under the hood, the models are updated constantly. Once again, if you have a prompt that you think will work really well for one specific model, and you're just trying to use it for the next better model, often the model will change out from under you, and those same prompts will stop working.

What Guardrails AI Does

What Guardrails AI does, in this framework of building more reliable generative AI applications. It's a fully open source library that, first of all, offers a framework for creating these custom validations that we talked about in the previous slide. The second is that it offers the orchestration of prompting and verification and re-prompting or any other failure handling policies that you've implemented. It is a library and a catalog of many commonly used validators that you might need across your use cases. We're going to dig into a few of them later. Finally, it's a specification language for communicating requirements to LLMs, so that you're priming the LLM to be more correct than it ordinarily would, by taking some of the prompt engineering pain away from you. How does each guardrail actually work under the hood? We talked about PII removal. We talked about executable code. Guardrails by themselves really are any executable program that you can use for making sure that your output works for you. One common thing that we see here is like grounding via any external system. For example, if you want to make sure that your code is executable, or if you generate a SQL query for some natural language question, making sure that that SQL works for you by hooking it up to your database or a sandbox of your database, and making sure that there's no syntax errors, the output that you get is actually output that works for you. You can use rules-based heuristics for making sure that your LLM output is correct for you. For example, let's say you're using your LLM to perform data extraction from a PDF. You read the PDF and you generate unstructured data. You can make sure that any unstructured data you generate follows rules that you expect from that system. Interest rates must always be followed by a percentage sign. Dollar values must always be between x and y ranges. You can use traditional machine learning methods. For example, you can use embedding classifiers, decision trees, K-Nearest Neighbors classifiers, to make sure that any output that you get is correct for some constraints. Let's say you have a previous treasure trove of data that you know is correct. You can use more traditional ML methods to make sure that the new data that you extract is similar to the data that you know is correct. You can use some high precision deep learning classifiers. This essentially entails making sure that you're not using GPT scale models, but you're still using a model that you can reliably host and serve. That can act as a watchdog to make sure that your output is correct and doesn't violate any concerns. Finally, you can use the LLM as a self-reflection tool. Making sure that you ask the LLM to examine the response that you got, examine the correctness criteria that you specified, and make sure that the response respects that correctness criteria.

I also talked about handling invalid outputs, and I talked about re-asking a little bit. Guardrails, the open source framework, offers a bunch of policies that you can configure on how you want to handle your validators failing on your verification suite. For example, let's say this is the response that you end up getting, let's say you're extracting a JSON with two values, one is transaction fee, and one is the value of the transaction fee, which is 5, but you have some guardrail, which is a very easy rule-based guardrail here, which essentially always inline checks that the value is less than 2.5. You can handle this using a bunch of different policies that you configure. We talked about re-asking where there's a new prompt that Guardrails construct for you. For example, you're an assistant, correct your previous response, which is incorrect because of XYZ reasons. Then, finally, send that response over to the LLM, and end up getting the new response, which in this case is transaction fee and 1.5. You can programmatically fix any output. This isn't always possible, especially for a lot of the more complex, more sophisticated guardrails that we have. In this case, for simple rules-based guardrails, you can often just programmatically fix this to, for in this case, like the maximum value of your threshold. You can filter any incorrect or offending values. In this case, you don't throw away the entire LLM response, you essentially make sure that you're only getting rid of the incorrect value of 5. This also works for string outputs. Let's say you have a large paragraph, and some sentences within that paragraph are hallucinated, or incorrect, or contain profanity, or whatever. In that case, instead of throwing away the entire paragraph, you can basically just remove those few sentences that are incorrect, make sure the fluency of your overall passage isn't affected, and generate a new response. You can respond with a canned policy, or a statement about why your response is incorrect. For example, "Sorry, no appropriate response possible." This is more appropriate for using when you essentially make sure that your cost of getting some requirement wrong, is so substantial that not answering the customer's question is actually a better compromise, in this case. This is one of my favorite ones, which is noop. You pass the incorrect output as is to your customer. This is when this isn't a very sensitive requirement for you. At the same time, every single guardrail that fails is logged for you. In this case, what you end up getting is very rich metadata about every single response that is served for you in runtime, and everything that's right or wrong about that response. You can use this to build better systems, train a better model. We talked about how hard it is to create a dataset for model training. You can use that for creating your model training dataset. Finally, you can also raise an exception, in which case you can handle that in your code or programmatically.

Example: Internal Chatbot with 'Correct' Responses

I want to now walk through some applications of, let's say, you're building an internal chatbot and you care about getting correct responses from that chatbot every time you use it. Let's say you have a mobile application and you want a chatbot that is able to look at the help center articles of your application, and that anybody can come in ask an English language question, and then you answer that question. This is often referred to as the Hello World of generative AI applications which is a chat your data app. Then the correctness criteria that you care about, first of all, don't hallucinate. Second is, don't use any foul language. This is table stakes, like don't swear at your customer, basically. Finally, don't mention any competitors. If somebody asks you like, who is the best burger joint in town, don't say Burger King when you're McDonald's. In this case, we basically talk about implementing three different guardrails to make sure that this problem works for us. Essentially, we solve this problem by implementing provenance guardrails, which contain checking for embedding similarity with any code that you have. This essentially ensures that any output that is generated is similar to your source content. Making sure that any output that you have is classified to be correct using some NLI or natural language inference model. Finally, using LLM self-reflection.

How Do You Prevent Hallucinations?

We listed out this criteria of, how do you prevent hallucinations? This is a pretty complex problem, is a very active area of research at this time. Often the question is like, ok, I can't really control my model, how do I actually in practice correct hallucinations? Add guardrails. We have this thing that we like to call provenance guardrails, where provenance guardrails, essentially, make sure that every single LLM utterance that is generated has some provenance in the source of truth that it came from. These guardrails are active and available for you to use today in the open source. Why this ends up being a problem is that these models are often so powerful and trained on the entire internet that even when you give them, "Ok, these are my help center articles. Make sure that you don't respond with anything that isn't in the help center articles." The model will often quote things from there, but then use its general knowledge, or quote something that it has memorized from the internet. In spite of creating retrieval augmented generation applications that try to constrain the outputs of the model, you still end up getting hallucinations in practice. Provenance guardrails act as a way to do inline hallucination detection and mitigation while you're running and building an application.

Example: Getting Responses with 'Correct' Responses

Let's see what this looks like in practice as you're building something. By now we're familiar with our diagram that we went over earlier. I think the only difference is that this time around, our verification suite consists of provenance, profanity, and peer institution. Making sure that every utterance has some source and isn't hallucinated. Making sure that there's absolutely no profanity in your output. Making sure that there's no reference to peer institutions. As you can imagine, all of these are complex problems by themselves. It's almost as hard to solve for these safety issues as it is to build an application. All of these are guardrails that are available for you in the open source today, because we've done the hard work of doing the machine learning and solving for these ML problems in the open source. Let's say you have the system and a user comes in with a question of, how do I change my password? If you're familiar with building AI applications, what this turns into is a prompt, where you're force going to prime the LLM by saying something like, you're a knowledgeable customer service representative. Somebody has a question of like, how do I change my password? Make sure you answer this question to the best of your knowledge. Then you give it a list of articles that it is ok to answer from. You're going to be like, in my help center articles, these are the articles that might be relevant. As an LLM, reason over these articles and make sure that the output that you get answers the customer's question to the best of your ability.

Let's say, the initial output that you end up getting looks something like this, where you have some raw LLM output that says, log into your account, go to user settings by clicking on the top left corner, and click, Change Password. This is a toy example. Essentially, settings aren't at the top left corner of your application. This is somewhat obfuscated, but this is an actual real example of someone that I was working with that was using Guardrails for their use case where the chatbot would hallucinate, where certain components of your application should be, even though it has no ability to visually look at your application. These types of hallucinations often end up being pretty common, and end up misleading your customer or don't end up giving useful responses to your customers. When this output passes through your verification systems, yes, there's no profanity in it. There's also no reference to any peer institution or to a competitor of yours. Your provenance guardrail would fail because that utterance does not have any bearing in the source of truth or in the articles that you provided your LLM. In this case, because we said re-asking as our error correction policy for provenance, a re-ask prompt is automatically generated where a simplified version of that re-ask prompt looks something like this. You talk about specifically what was wrong in your response, what the error looked like, as well as the newer instructions. In this toy example, we end up getting the correct response, which is basically, click on the menu to go to settings. This time around your verification passes and you're able to route that output out to your LLM.

Complex Workflows with Guardrails

This is what happens on a one-shot workflow where you have a single LLM call, and you want to safeguard that LLM call with guardrails. Now, we come back to our original motivation of, I want to build really complex systems where I want to use my ML modeling APIs, but I want to make sure that my ML modeling APIs are reliable and secure and safeguarded. What this ends up looking like is that, instead of a single LLM API call, you now have two LLM API calls, each of them have specific prompts that work for them. Typically, the output of one LLM API is fed as the input to the prompt of the other API. In practice, this leads to the issue of like compounding errors as well, as you can imagine, because you essentially don't have water tightness on any of these APIs. As you move down the chain, you end up getting more incorrect responses. The guardrails way of the world in working with these systems essentially, is making sure that you have verification logic that surrounds every single LLM API that you're making, so that the first output first passes through your verification suite, and only clears that suite once you know that that output is functionally correct. If we fail, we obviously go through our re-prompting strategy. If it passes validation, only then you go on to the next stage, where you then again go through this verification logic, and make sure that you send your response back to the AI application only on passing the verification logic. If we zoom out from this framework, overall, this reduces the degree of like compounding error, and on the entire workflow level, you end up having applications that are much more robust to the inconsistencies with working with LLMs.

Example: Guardrails for Customer Experience Chatbot

I've included some example of what types of guardrails you typically end up needing as you're building a customer experience chatbot. We talked about like provenance and hallucinations. Typically, any unviolatable constraints that you have, like making sure that you can trust the output, making sure that your output is compliant. Or that there's brand safety and you aren't violating any constraints of like not using a language that wouldn't be supported by your brand. Making sure that you're not leaking any sensitive information. In general, making sure that your output has reliability, as well as data privacy. In practice, what this might end up looking like is that you only use vetted sources for building trust. You can encode guardrails for specific laws. For example, if you're a healthcare company, making sure that you're never giving any medical advice, because as an AI system, you're not allowed to give medical advice. Never using any angry or sarcastic language for your AI system. As well as making sure that you're able to control the input and output of any information that is sent into the LLM or retrieved from the LLM, and there's no leakage of any sensitive data. Finally, only responding within category boundaries. Let's say you're building a customer support chatbot, if somebody is just like chatting with you about something that doesn't pertain to the boundaries of that system, making sure that you're guarding against like proper use of any application that you build.

Examples of Validations

More examples of validations that we care about. We talked about hallucination, of course. Never giving any financial or healthcare advice is another common criteria. As you can probably expect, this is a pretty complex machine learning problem of, in the first case even detecting what financial advice is or what medical advice is. Never asking any private questions. Never asking the customer for, give me your social security number or any private or sensitive information. Not mentioning competitors. Only making sure that each sentence is from a verified source and accurate. No profanity. Guarding against prompt injection, as well as never exposing the prompt or the source code of your application.

Guardrails AI is a fully open source framework. You can use it for creating any custom validator, as well as looking through the catalog of validators to plug into your AI applications, and making them safe and performant and reliable. You can use it for orchestrating the validation and verification of your AI systems. You can use it as a specification language to make sure that you're communicating your requirements to the LLM correctly. To learn more, you can check out the GitHub repo at github.com/ShreyaR/guardrails. Our website is guardrailsai.com.

Questions and Answers

Participant 1: I liked the name Guardrails, you somehow arranged the Wild Wild West into a path that you can actually control. When you create the validators and now your input is going to go to the validator, how do you route it to the appropriate one? You have some logic, let's say, I have three validators, I'm going to send to the first one, then I'm going to send to the second one, and I'm going to send to the third one, or how do you ascertain which one goes first?

Rajpal: There's two ways of doing it today. I think the first one is that all validators are executed parallelly. You send your output, or your input to every single validator at the same time. I think in addition to that, we also have a specification language that allows you to stagger the execution of those validators. Some validators are typically more expensive. I talked about hallucination excessively. We have multiple different ways of targeting hallucination. Typically, there's this tradeoff between the fastest and cheapest methods to run will also not be the most accurate methods. You can stagger them in a way so that you run the fast and cheap one first, and only if it fails, do you go down to the more expensive method. We have this specification language that allows you to pipeline them in different ways.

Participant 1: Is that why you actually have MapReduce them, where your mapper is doing things in parallel, and then the reduce takes the end?

Rajpal: The MapReduce example, I included, is another common example of building AI applications. A common issue that folks who are building with these systems have to wrangle is their context limit. With GPT-4, for example, or with most common GPT APIs, you can only send 4000 tokens, and let's say you have a massive book. In that case you have to break that book down into 4000 tokens each, do your map stage over each book, and then do a final reduce stage using the LLM. That was a system that uses that MapReduce framework with Guardrails included at the mapping stage as well as at the reducing stage.

Participant 2: Thank you, again, for that thought. I was going to ask a question about context windows [inaudible 00:39:55] because of course, every time that you have to re-prompt [inaudible 00:40:00] the context window. So my question I'm going to focus on is about cost, actually. So you mentioned that there's a guardrail without a procedure sometimes you re-ask the question [inaudible 00:40:14]. What has been your [inaudible 00:40:19] re-prompting to make it more accurate?

Rajpal: I think there's two points I would highlight with respect to cost. All of the re-prompting is configurable. If you're working with a guardrail that you don't care about as much, or there's some cost to getting it wrong, but you can use it using other methods. You don't always have to re-prompt, you can just filter out the incorrect values or use a programmatic fix when available. Or just be like, ok, maybe I can't answer this query. Typically, what I've seen people do is only configure re-prompting as their policy when it's a guardrail, where, if this guardrail is wrong, or if this check fails, then my output just straight up isn't useful to me. For example, I really like the idea of healthcare advice or financial advice as one of those things where some of the folks, for example, that we work with, they're like, the cost of accidentally including healthcare advice in my output is so severe to me as an organization, when I might as well not respond, in which case I'm not giving value to my customer, or I might take that extra hit of re-prompting. Those are typically those subsets of guardrails on which people configure re-prompting. This is not as much on the software level, but more on the scope of problem level, a lot of the places where people end up using these systems are where the alternatives are typically way more expensive. Often, you typically have to wait in line for an hour to get an answer to your question, in which case companies are often ok with taking on that extra cost of re-prompting when necessary.

Participant 3: About your verification suite, how do you handle false positives?

Rajpal: A lot of the guardrails that we have are ML based as well, in which case they come with their own irreducible error or their own possibility to basically be wrong sometimes. I think how I like to look at this problem is, on some level, what we're doing is ensembling, which is basically this technique of making different machine learning models work together, where each model has different strengths and weaknesses. My favorite analogy to use for this is that you're stacking together different sieves, where each sieve has holes in different locations. You end up getting systems where the stack of sieves is way more watertight than each sieve individually would be. For example, for provenance or for our anti-hallucination guardrails, we have a bunch of internal testing, which allows us to estimate what the efficacy of the system is. The idea is that you mix and match a few of these together and ensemble the guardrails, that with the LLM ends up getting you to, overall, a much more robust system. That said, you're working with machine learning, you're never going to have zero error. The idea is to drive down the error as much as possible, using the tools that are available to us.

Participant 3: Once you start getting false positives, will that translate somehow into a new guardrail. I don't know. [inaudible 00:44:06] dynamic guardrail or something like that where [00:44:11].

Rajpal: I'll ground my answer in the example of provenance. In the example of provenance, I talked about embedding the similarity versus an NLI classifier. With embedding the similarity, which is the fast, cheap, and not as accurate method of detecting hallucination, what we end up finding is that it had a lot of false positives. Then, that allows us to figure out like, we have some estimation of the uncertainty of that system. What we end up doing basically is like K-Nearest Neighbors classification, and then what we end up seeing is like, based on our KNN classification, if the distance is greater than some threshold, then we probably don't have a very good estimate for this point, in which case, you then route it. The failure policy for this guardrail is to send it to the more performant but then much more expensive guardrail, which is more accurate. That failover and failing onto different systems is part of it.

Participant 4: If along with having guardrails with different style, have you considered maybe fine-tuning the model with an updated cost function that incorporates the data.

Rajpal: Our approach to that system is basically to make it very easy for you as a developer to log the outcomes of those guardrails. Where, you're running this live production system, you're serving thousands of requests every day, so you end up getting over a month of running this, like a decent dataset with features about what works for each request or not. You can directly use that dataset, which we make it very easy to export, like for your fine-tuning. It's a very interesting point where you can use it basically in your loss function of training a model and basically add it to your loss and basically get a much more representative loss. I haven't seen anybody do that today. I don't see a reason why not because the guardrails, like I said, are independently implemented, like modules basically. Yes, theoretically, you could.

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Recorded at:

May 01, 2024

Shreya Rajpal

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