RAG works when knowledge is ready
Retrieval augmented generation helps an AI system answer from your documents instead of relying only on model memory. It works best when the source knowledge is current, clear, structured, and trusted.
Documents need preparation
Documents need ownership, naming, chunking, access rules, and cleanup. Policies, product docs, internal guides, and support answers should not contradict each other.
Retrieval must be evaluated
You need to know whether the system retrieved the right documents before the model wrote the answer. Test retrieval with realistic questions, synonyms, old policy names, and edge cases.
Permissions matter
A knowledge assistant should only answer from sources the user is allowed to access. RAG is a knowledge system with rules.
Citations build trust
Users need to see where an answer came from so they can verify claims and trust the system enough to use it again.