Choosing a RAG Architecture Only When Evidence Requires It
Explore how to evaluate retrieval-augmented generation (RAG) architectures and decide when to adopt complex retrieval methods. Understand relevance, coverage, latency, costs, and operational risks, and learn to justify architecture decisions based on measured evidence and system evaluations.
Retrieval-augmented generation (RAG) is an architectural boundary here, not a specific technique: a system retrieves evidence from approved sources and inserts it into the model context so generation can ground claims and emit citations. It does not include training a model, changing the model, or relying on hidden model memory to satisfy a policy requirement.
The baseline option is simple retrieval: a single query fans out to one index, returns top-k items, packs them into the context window, and asks for an answer with citations pointing back to what was retrieved. Justify more complex variants only when simple retrieval can’t meet relevance or coverage targets. Those variants are hybrid retrieval, an explicit reranking step, and routing the query to the most appropriate index when sources behave differently, policy documents versus structured records, for instance.
This diagram shows the component-level differences between a simple retrieval flow and a flow that adds hybrid retrieval and reranking, including where latency and cost accumulate.
Decision dimensions that justify added retrieval steps
A citation only helps if the cited evidence actually supports the answer. Relevance means the retrieved items are on topic. Coverage means the retrieved set includes the specific clause, definition, or field-level record the answer needs, not just something nearby. Coverage also includes permission-aware visibility: the retrieval layer must ...