The hype around GenAI is settling into more serious consideration of how companies can build AI into their tech stacks and leverage AI to deliver more value for customers. At the same time, both costs and barriers to entry are dropping. Powerful, state-of-the-art large language models (LLMs) like Meta’s LLama 3 are increasingly available on an open-source basis. Meanwhile, managed service providers are entering the market to help companies with everything from data labeling to prompt engineering to human evaluation.

But there’s one thing a third-party provider can’t offer: a rich dataset unique to your organization. Your internal company knowledge is data gold, from proprietary code and the context and business logic behind coding decisions to process documentation, FAQs, and how-to guides. If your goal is to build an AI assistant that helps your developers as they create software or provides answers to employees the moment they need them, there’s no piece of the GenAI stack more crucial to your success than your data.

According to expert research, data quality is the most important factor that determines the performance of an LLM. And from our (admittedly biased) perspective, there’s no better method for organizing and optimizing your data than Stack Overflow for Teams.

Here are five reasons why Stack Overflow for Teams is made for GenAI.

High-quality data, structured for maximum utility

LLMs trained on up-to-date and well-organized data deliver more accurate, complete, and relevant answers. MIT Media Lab research has found that integrating a knowledge base into a LLM improves output and reduces hallucinations: industry jargon for incorrect results, which can range from small inaccuracies to completely invented answers.

Remember the classic computing maxim “Garbage in, garbage out”? It’s the same with LLMs. Train them on low-quality data, and their output will be garbage: answers that are unhelpful at best, misleading or totally incorrect at worst.

A well-built codebase and/or knowledge base represents the intellectual effort your employees have put in over years or even decades. This effort compounds as teams learn from their predecessors: building on their successes and drawing lessons from their missteps. The data it contains is accurate, well-organized, searchable, categorized by helpful metadata, and easy to update. Stack Overflow for Teams was made to help you build and grow that knowledge base.

The unique way Stack Overflow’s data is organized also makes the platform a natural fit for LLMs. As any developer knows, our platforms (both public Stack Overflow and Stack Exchange networks, and paid Stack Overflow for Teams instances) are structured in a Q&A format. Of course, that’s the same format in which users engage with an LLM: they ask a question and receive an answer. A dataset already structured around questions and answers helps train a model to provide useful answers to specific questions, as research from Cornell University has shown.

The quality of the data impacts not just the accuracy of an AI model, but also its capability at different sizes. The bigger the model you’re building, the greater the cost and time of training it. Similar dynamics are emerging in terms of hosting these models and running them after each question to generate answers, a process known as inference. Using smaller, lighter models can help keep your compute costs down.

As research from Microsoft has shown, using high-quality data, like Q&As from Stack Overflow, allows smaller models to perform far better than would be expected for their size. Employing this same approach on your internal GenAI apps could result in faster results and huge cost savings over time.

The big players in AI and LLMs trust our data

The biggest players in the GenAI space clearly understand the importance of data quality, too—that’s why Google is our first data licensing partner. Our newest offering, OverflowAPI, is a subscription-based API service that provides continuous access to Stack Overflow’s public dataset to train and fine-tune LLMs.

Google Cloud will integrate Gemini for Google Cloud with Stack Overflow, putting essential knowledge and coding help at developers’ fingertips. Google Cloud will also surface validated technical knowledge from Stack Overflow within the Google Cloud console. This will give developers easy access to trusted, accurate knowledge and tried-and-tested code backed by the millions of devs who have contributed to Stack Overflow’s public platform over the last decade and a half.

Announcing the partnership, Thomas Kurian, CEO at Google Cloud, said it brings “our enterprise AI platform together with the most in-depth and popular developer knowledge platform available today.”

Stack Overflow for Teams is a uniquely powerful system for organizing human knowledge and maintaining its quality in such a way that humans and LLMs can learn from it. Sure, you could train an LLM on your company’s documents, wikis, emails, instant messages, and code comments. But the LLM on its own will have no way of assessing the accuracy or freshness of information and no mechanism for selecting the best answer from a mess of potentially contradictory options. Train an LLM on your company’s knowledge base and codebase, however, and you give your users access to the accumulated wisdom and proven best practices of your entire organization.

Powerful features ensure knowledge health

Stack Overflow for Teams has key features that keep your company’s knowledge base up-to-date.

Content Health helps intelligently identify and surface potentially outdated or inaccurate knowledge—content that needs to change. Experts and content moderators can take action by reviewing, updating, or retiring knowledge that Content Health flags, rather than manually scouring the knowledge base for necessary updates.No more stumbling across a Wiki article from 2019 and wondering if the information still applies. Your teams and LLM will appreciate a better way to keep knowledge up-to-date in Stack Overflow for Teams.

It’s important to remember that, while LLMs are powerful technologies that have shown an incredible ability to understand, reason, and generate with language, they have no ability to discern between accurate and false information while training or retrieving data, and no capacity to understand when certain information might be out of date.

Voting is another way that Stack Overflow for Teams surfaces the most valuable, accurate information for users in search of answers. Upvoting a question, answer, or Article signals to the company that the content is interesting, helpful, and well-researched. Downvoting a post indicates that it’s poorly researched, contains incorrect information, or fails to communicate information.

Last but not least, our product allows users to add a wealth of context beyond just the questions and answers. Tags and comments provide a rich set of metadata that helps an LLM to better understand the nuances of how this information should be applied in your organization and where important connections and relationships exist between different Q&A couplets.

All the information we highlighted above is provided by your team, harnessing the wisdom of the crowd, or in this case, the wisdom of the community. Stack Overflow for Teams adds the crucial and necessary element—human judgment—to your knowledge base so your LLM can perform even better.

16 years of community-building experience

Stack Overflow has been honing our service—the world’s highest-quality knowledge base, curated by a community of expert practitioners—since 2008. Microsoft, Bloomberg, Expensify, Dropbox, and many more organizations around the world, across all industries, rely on us to capture and share knowledge, fuel collaborations, and make developers happy.

With this experience, we have pioneered best practices in community-building, collaboration, and knowledge sharing for our customers.

Wherever you are in your GenAI journey, a high-quality knowledge base delivers huge benefits, including laying the foundation for training an LLM on your proprietary knowledge or code down the road.