Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation is an AI technique that fetches relevant documents from an external knowledge source and feeds them to a language model before it answers. Rather than relying only on training data, the model grounds its response in retrieved, current, source-specific information.
Why It Matters
RAG lets AI answer from your own content and up-to-date facts, reducing hallucination and enabling citations. For search and support, it is what allows an answer engine to ground responses in trusted sources.
Common Mistake
Feeding the model poorly chunked or irrelevant content. If retrieval surfaces the wrong passages, the answer is confidently wrong, so retrieval quality matters as much as the model itself.