Retrieval-augmented generation, or RAG, lets an AI system find relevant company information before composing an answer. The quality of that answer depends far more on the retrieval system than most demos reveal.
Start with the questions
Collect the real questions users ask and identify which sources contain authoritative answers. This becomes the first evaluation set and prevents the project from becoming a generic document search demo.
Prepare knowledge deliberately
Remove duplicates, preserve useful structure, apply metadata and choose chunk boundaries that reflect how the content is understood. Poorly prepared documents create incomplete retrieval and confident wrong answers.
Retrieve, rank and ground
Search should combine semantic relevance with business filters such as product, geography, date and permission. A ranking step can then select the best evidence.
The response should cite its sources and clearly say when the available evidence is insufficient.
Evaluate before launch
Test retrieval accuracy separately from answer quality. Include ambiguous questions, outdated content, permission boundaries and requests with no valid answer.
Monitor unanswered questions after launch; they reveal both knowledge gaps and product opportunities.
A reliable RAG system is a governed knowledge product—not a folder of PDFs connected to a chatbot.