Key takeaways:
- AI models are only as good as the data they’re trained on, and many organizations are unknowingly feeding their models bad data.
- A single source of truth in the form of a unified, updated knowledge base allows your AI to deliver high-quality results and realize business value.
- Human validation of AI output ensures the accuracy and trustworthiness of AI models.
- Stack Overflow for Teams will help you grow a high-quality knowledge base to ensure you get maximum business value from your AI projects.
A crisis is brewing behind organizations’ widespread enthusiasm for adopting AI tools, training AI models, and even rebranding themselves as AI companies. If you’ve been reading our blog and articles over the last year, you probably know what it is: AI models are only as good as the data they’re trained on, and many organizations are unknowingly feeding their models bad data.
In this article, we’ll get into how low-quality and/or unstructured data (outdated wikis, chats, and uncaptured institutional knowledge) leads to unreliable AI output. We’ll explain why an organization’s most valuable AI asset is not the model itself but the underlying data.
Read on for answers to questions like:
- How do I ensure my AI models aren’t derailed by bad data?
- Why does my organization need a single source of truth for its AI initiatives?
- What is an internal knowledge base and how can it address data quality challenges?
- Why is human-validated data crucial for improving the accuracy and trustworthiness of AI output?
When bad data happens to good models
You can’t build or run an AI model that adds business value if you’re training it on disorganized, incomplete, outdated, or otherwise junky data. On an episode of Leaders of Code, Don Woodlock, Head of Global Healthcare Solutions at InterSystems, compared junky data to an out-of-tune guitar: No matter how good the guitarist, a poorly tuned instrument won’t produce much worth listening to.
“You can be an awesome guitar player, but an out-of-tune guitar is just not useful,” he said. “So step one is to get that tuned and then you can layer on top of that some great playing and songs. That’s the way I think of data. Step one is really to have a good set of data that you build everything on top of. And if you don’t, there’s not a lot of places you can go and be successful.”
When models trained on low-quality data cough up low-quality results, developers lose faith in AI tools. According to the 2025 Stack Overflow Developer Survey of nearly 50,000 developers from 177 countries, 84% of devs use or plan to use AI tools this year, up from 76% last year. At the same time, though, developer trust in those tools is falling. Only 29% of respondents this year report trusting AI outputs to be accurate, down from 40% last year.
Why the distrust? Because developers know the answers AI provides are often inaccurate. More developers actively distrust the accuracy of AI tools (46%) than trust it (33%), while only 3% report that they “highly trust” the output.
Why your knowledge management approach might doom your AI model
Disorganized knowledge creates a number of pain points for your organization, from wasted time to user frustration to serious security and efficiency gaps.
But the biggest headaches may stem from AI models trained on disorganized, unstructured data from a mess of sources including outdated wikis and chaotic Slack threads. Models trained on this garbage data are prone to hallucinations that, at best, reduce the value they can offer your organization. At worst, AI hallucinations lead to serious downstream consequences, including legal repercussions.
Expert research has shown that data quality is the primary factor behind the performance of a large language model (LLM). Models trained on up-to-date, well-organized data deliver more accurate, complete, and relevant responses to user prompts. And the MIT Media Lab has found that integrating a knowledge base into an LLM improves output and reduces hallucinations.
Keep in mind the results of our 2025 survey: While developers see value in the speed and automation offered by AI tools, they are skeptical of the quality and reliability of AI-generated results. After all, AI-generated answers aren’t knowledge. However useful AI tools are for your teams, they’re no substitute for a robust knowledge management strategy.
Why a single source of truth is necessary for AI success
When knowledge is scattered across disconnected platforms, outdated documents, or conflicting Slack threads, AI models inherit that chaos. Inconsistent inputs lead to inconsistent outputs: hallucinations, inaccuracies, or biases.
To avoid these pitfalls, AI models need a single, reliable dataset to learn from and reference. They need a single source of truth (SSOT): a centralized repository of accurate, human-validated information that the whole organization can rely on. A unified, up-to-date knowledge base reduces ambiguity and confusion, reinforces alignment across teams, and ensures that every answer or prediction is built on shared, validated information.
But even an SSOT is only as strong as the people and people-centered processes that keep it alive. Human validation—experts reviewing, correcting, and curating the data that feeds AI models—allows us to trust the output of those models. Machines can process information at scale, but only humans can confirm nuance, context, and accuracy. With a human-verified SSOT behind their AI systems, organizations can make their systems more consistent, transparent, and trustworthy.
It’s also important to note, as we did on Leaders of Code, that many organizations overestimate the quality of their data and its readiness for use in AI systems. A clean, centralized knowledge base is an investment in future AI projects; getting your house in order now will yield benefits in the future.
The Stack Overflow advantage: Human-validated knowledge
Your internal company knowledge—from proprietary code and the context and business logic behind coding decisions to process documentation, FAQs, and how-to guides—is a priceless business asset. If your goal is to build an AI assistant that helps your developers create software or delivers answers to employees the moment they need them, there’s no piece of the AI stack more crucial to your success than your data.
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 helps 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 every developer knows, our public platform is structured in a Q&A format. 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, according to research from Cornell University.
Stack Overflow for Teams includes powerful features that help you keep your knowledge base healthy and self-sustaining:
- Content Health helps identify potentially outdated or inaccurate knowledge. Moderators can take action by reviewing, updating, or retiring knowledge that Content Health flags, rather than manually scouring the knowledge base for necessary updates.
- Voting surfaces the most valuable, accurate answers to user questions. Upvoting content communicates that it’s pertinent, helpful, and well-researched. Downvoting indicates content that is irrelevant, incorrect, or hard to understand.
- Tags and comments represent rich metadata that helps an LLM absorb context and learn the relationships between different Q&A couplets.
- Human validation allows us to trust the output of AI models. That’s why Stack Overflow for Teams uses a human-centric validation approach, with people firmly in the loop of collecting, adjusting, and curating data to fuel AI systems.
Getting started: A practical roadmap
Ready for actionable, step-by-step guidance on how to get started with Stack Overflow for Teams? Check out 10 of our most frequently asked questions about the platform.
From there, explore our guide to rolling out Stack Overflow for Teams for your team, then dive into a day in the life of a Stack Overflow for Teams moderator.
Looking for a simple, easy-to-reference user guide? We have you covered.
Wondering how to measure success in your first two weeks of using Stack Overflow for Teams? Start here.
There are plenty of other resources, including industry-specific use cases and helpful demos, in our learning center.
Knowledge is a competitive advantage
AI will only ever be as powerful as the knowledge you give it. If your internal data is scattered across Slack threads, buried in outdated wikis, or tucked away in someone’s head, your AI initiatives will reflect that chaos. But when you invest in a single source of truth—one that’s structured, current, and actively validated by experts—your AI systems can become reliable engines for productivity and innovation.
The payoff isn’t just better answers from your AI tools; it’s also faster onboarding, fewer duplicated efforts, reduced support costs, and more confident decision-making across the organization. Stack Overflow for Teams helps organizations make that shift. By capturing institutional know-how in a transparent Q&A format and reinforcing it with human validation, it ensures your models learn from the best version of your collective intelligence. Clean data builds trustworthy AI, and trustworthy AI drives measurable ROI.