A production retrieval-augmented application needs an authorized knowledge source, suitable retrieval, grounded answers, and an evaluation process. Query decomposition, hybrid search, and reranking are options to test against specific failures rather than mandatory layers for every deployment.
Choose chunk boundaries using the documents and questions the application actually receives. Section-aware chunks can preserve useful context, while overlap may help when facts cross boundaries. Compare approaches on representative queries and inspect omissions, duplication, and context cost instead of assuming a universal winner.
Query decomposition can help with a question containing several distinct subquestions. Compare it with direct retrieval, retain the original question for answer generation, and check that the decomposition has not changed its meaning. Additional searches also add cost and can introduce irrelevant material.
A reranker can reorder retrieved candidates before generation. Evaluate answer quality and latency on a fixed dataset before adding one, and compare it with improvements to chunking or retrieval filters. Report the evaluation method before assigning any percentage gain.
Hybrid retrieval combines semantic and lexical signals, which can help when a question includes an exact name, identifier, or error code. Tune score handling and result limits against representative queries. Apply the same access restrictions to every retrieval path.
Build an evaluation set with ordinary questions, missing evidence, ambiguous wording, outdated documents, and access-control cases. Measure retrieval and answer behavior separately, review regressions, and define acceptance criteria appropriate to the risk. Do not claim deployment-wide coverage without the corresponding execution evidence.
Production RAG is not a solved problem, but the engineering patterns are well-established. The investment in retrieval quality pays compound dividends as your knowledge base grows.
