The importance of data quality

GenAI can help with both sides of the conversation: the prompt and the response.

GenAI is undeniably powerful, but its name aside, it can’t make something from nothing. Because an AI model is trained on the information you provide, its output will only ever be as good as its input.

If a model has access to complete, accurate, and up-to-date information, the AI will return more complete, accurate, and up-to-date information. If the training information is incorrect or outdated, on the other hand, the AI will return incoherent or incorrect answers. These are called hallucinations, and they can create chaos and confusion for your teams.

A solid knowledge management strategy is the foundation for AI success

Integrating a well-structured knowledge base into a large language model (LLM) reduces hallucinations and improves answer quality, according to research from MIT. Avoiding hallucinations requires a body of knowledge that is:

• Accurate and trustworthy, with information quality verified by knowledgeable users.
• Up-to-date and easy to refresh as new data/edge cases emerge.
• Contextual, meaning it captures the context in which solutions are sought and offered.
• Continuously improving and self-sustaining.

A comprehensive knowledge management strategy is therefore crucial for ensuring data quality and making the most of your AI initiatives.

Knowledge management allows your organization to:

• Build trust in your AI programs through human validation of answers/output.
• Ensure accuracy and transparency with peer-reviewed, peer-validated sources.
• Foster continuous learning and growth around your AI initiatives.

Iterate and improve

A knowledge management approach that enables discussion and collaboration allows you to work with colleagues to vet the AI’s responses and refine prompt structure to improve answer quality. This interaction is a form of reinforcement learning in AI: humans applying their judgment to the quality and accuracy of the AI-generated output to help the model improve.

Just as the quality of your input affects the quality of your output, how you structure your queries impacts the quality of your results. Prompt engineering—that is, understanding how to structure queries to get the best results from an AI—is both an emergent skill set and an area where GenAI can help with both sides of the conversation: the prompt and the response.

Stack Overflow for Teams is purpose-built to capture, collaborate, and share your organization’s internal knowledge, from technical guides to product requirements to the context and reasoning behind architecture decisions and product releases. AI-powered search and chat will help users draw even more benefit from your knowledge base.