RAG

RAG systems combine retrieval with generation. They may fetch relevant documents or passages, then synthesize an answer from that context. This makes source quality and answer-ready content important. The technical layer matters because AI systems need usable context, but technical access is not the same as good interpretation. Clear entities, current pages, consistent claims, and useful source context still determine whether the answer helps a buyer.

If the retrieved context is vague, outdated, or overly problem-heavy, the generated answer may inherit those weaknesses.

A RAG workflow pulls a help article about setup errors and uses it to answer a buyer question about implementation difficulty.

Using RAG as a synonym for all AI search instead of a specific technical pattern.