At Stack Overflow, we know that capturing and preserving institutional knowledge is a constant challenge for every company. Organizations need a context-rich, continually updated knowledge base and a knowledge management system to centralize, validate, and refresh information in a collaborative way. Without a centralized knowledge base, valuable insights remain fragmented or get lost in the cracks between teams. Inefficiency, rework, and frustration are the inevitable results. On the other hand, a centralized repository of high-quality information prevents knowledge loss and encourages knowledge-sharing and collaboration across teams, unlocking innovation and productivity gains throughout the org.

Now that GenAI applications are top-of-mind across so many industries, we can see another set of benefits of a clean, centralized knowledge base: an opportunity for vastly improved AI training, leading to more powerful solutions and more satisfied users. A structured, well-organized knowledge base is essentially data gold: a top-notch dataset to fuel future internal or customer-facing AI projects.

As AI takes on a bigger role in enterprise operations and the overall AI landscape evolves with startling speed, data fuels its growth. But your organization’s data may be a long way from powering an AI application that will add value for your employees and/or customers. As experts in GenAI have told us, plenty of companies overestimate their readiness to turn their internal data into fuel for an AI project.

In this article, we’ll explain the importance of a clean, centralized, and continually updated knowledge base in building and leveraging AI applications.

How data quality determines model performance

Expert research shows that data quality is the most important factor in determining the performance of a large language model (LLM). In other words, models trained on up-to-date and well-organized data deliver more accurate, complete, and relevant answers than models trained on lower-quality data. Research from the MIT Media Lab has found that integrating a knowledge base into a model improves output and reduces hallucinations (that is, incorrect results, from subtle inaccuracies to answers pulled from thin air).

You’re almost certainly familiar with this timeless maxim: “Garbage in, garbage out.” A classic rule of computing, it applies to AI models as well. Train them on low-quality data and their output will be garbage: useless at best, actively damaging at worst.

A well-built codebase and/or knowledge base represents the intellectual effort your employees have put in over years—even, potentially, decades. This effort compounds as teams learn from their predecessors: building on their successes and drawing lessons from their missteps. Your data must be not only accurate and easy to update but also well-organized, searchable, and categorized by helpful metadata tags. After all, the value of knowledge is severely limited if you can’t surface that knowledge when and where you need it. And it goes without saying that if your institutional knowledge is scattered across different channels and siloed teams (some of it in docs; more in Slack, email, or project management software), any AI model you train on that data will have to labor to connect disparate sources of information, which may not produce the full picture or context you need.

The structure of your data can determine how naturally and effectively the AI model is able to engage with users. A dataset structured around questions and answers, like we’ve built at Stack Overflow, helps train a model to provide useful answers to specific questions, as research from Cornell University has shown. That’s because a Q&A format is how users engage with an LLM: they ask a question and receive an answer.

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, the more it costs to build and the longer it takes to train. Research from Microsoft has shown that using high-quality datasets, like Q&A content from Stack Overflow, allows smaller models to outperform expectations for their size.

Your data is your most important AI advantage

Whether you’re building an AI-powered assistant trained on your codebase to boost your developers’ productivity or an AI chatbot to help customers self-serve answers to their questions, a successful AI application starts with good data.

Clean, up-to-date data in a centralized location is the first step, but the next step is leveraging AI to harness that data to help your organization meet its goals. We mentioned above that your organization’s institutional knowledge is data gold: a deep, broad dataset unique to your org, packed with proprietary code, the context behind coding decisions, and the business logic driving best practices, plus docs, wikis, how-to guides, and FAQs compiled and vetted by your experts. Models trained on this institutional knowledge can deliver impressive productivity gains and other benefits for your employees and customers.

Here are some use cases for AI around internal, organizational knowledge:

• Internal chatbot or agent that answers department-specific questions, like payroll and IT FAQs
• Customer service chatbot or agent that helps internal teams surface answers to customers’ questions quickly and easily
• Onboarding assistant that helps new hires get up to speed quickly and allows them to self-serve answers to their questions.

Set up for success

In the AI era, your company’s data is both a foundational resource (the fuel that keeps you moving) and a competitive asset (the quality that sets you apart). Getting your internal, institutional knowledge to a place where it can support AI use cases like those we’ve mentioned here is one way engineering leaders can position their orgs for success in this era of rapid innovation and change. Investing in data quality and bolstering your knowledge-sharing infrastructure is how you get there.