Contextual Reranking for RAG: Query Context, Candidates, and Tests

Contextual reranking evaluates retrieved candidates against a standalone question resolved from the current conversation, rather than relying on ambiguous follow-up wording or indiscriminately adding stored preferences. It should be distinguished from query resolution, chunk enrichment, and candidate retrieval because reranking can only reorder evidence already in the candidate pool and cannot recover missing documents. Evaluation should preserve scope and permissions before ranking, use stable document and chunk identifiers with version tracking, and record both original and resolved queries. Recommended testing compares a fixed baseline with query resolution, reranking, and their combination while holding the corpus, labels, candidate budget, and answer model constant. Key measures include candidate recall, reciprocal rank, top-result quality, answer support, latency, and token use, while unanswerable questions should be assessed separately for appropriate abstention. Failures should guide improvements: absent evidence suggests ingestion, filtering, chunking, or retrieval issues; buried evidence may justify reranking; and incorrect answers despite available evidence indicate generation or interpretation problems. The included metric helper deterministically calculates recall and reciprocal rank while handling duplicates and invalid cases, but no new reranker benchmark was conducted.

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