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RAG done right: why retrieval quality decides answer quality
Retrieval-augmented generation explained, and the design choices that make it accurate, citable and trustworthy.
10/9/2026
Retrieval-augmented generation (RAG) lets an AI model answer using your own documents rather than only what it learned in training. It's popular in regulated industries because answers can point to their sources. But a RAG system is only as good as its retrieval.
How it works. Documents are split into chunks and indexed. When someone asks a question, the system retrieves the most relevant chunks and passes them to the model, which writes an answer based on them.
Where RAG systems go wrong - Poor chunking splits tables or clauses and loses meaning - Weak retrieval returns related but wrong passages - Stale content answers from outdated policies - No citations, so answers can't be checked - Access gaps, so people see documents they shouldn't
What good looks like - Chunking designed around your document types - Hybrid search that combines keyword and semantic matching - Citations on every answer, linking to the source passage - Permissions respected at retrieval time - A "don't know" response when evidence is thin - Regular testing against a set of real questions
Retrieval is a search problem before it is an AI problem. Invest there first.