Company context changes the problem
A generic chatbot can help with brainstorming, but it does not automatically understand your policies, contracts or support history. A business knowledge system needs useful sources, appropriate access permissions and a way to handle stale information. The challenge is product fit as well as answer accuracy.
Design retrieval before polishing prompts
Retrieval-augmented generation combines information retrieval with generated answers. If the system cannot find the right information, a carefully worded prompt cannot rescue it. Start with the sources, the questions users ask and the evidence an answer should contain.
Prepare knowledge for the questions
Document hierarchy, chunk size, metadata and embedding choices influence what can be retrieved. Keep the context needed to interpret a passage, and design the index so relevant information can be found without losing its provenance or access boundaries.
Define behaviour when evidence is missing
Prompts should explain how to use retrieved context, how to identify sources and when not to answer. Make uncertainty explicit and provide a path to human review. A system that admits missing evidence is more useful than one that confidently improvises a company policy.
Make quality inspectable
Track which sources supported an answer, whether the response matched those sources and whether the user found it useful. Build representative evaluation questions and review failures. Reliable RAG development is an ongoing quality loop, not a single chatbot launch.
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