Reranking
Reranking uses a second model to score search results again. It compares the question and each found document more closely, then changes their order.
Another example
Vector search finds 50 possible passages for one question. A cross-encoder moves the best answer higher. Reranking cannot add a passage missing from those 50 results.
Main parts
- 01initial candidates
- 02pairwise relevance score
- 03reordered top-k
Use it when
Use this term after broad search when the first few results need a stronger relevance check.
Do not use it when
Do not use a reranker on an unlimited collection or let top-k scores hide missing candidates.
Name used in code
retrieve N → score pairs → return top K Before you ship
Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback.
Outcome: Use or evaluate Reranking to make AI behavior measurable and tied to evidence. User context: Vector search finds 50 possible passages for one question. A cross-encoder moves the best answer higher. Reranking cannot add a passage missing from those 50 results. AI method or concept: Reranking. Why it fits: Use this term after broad search when the first few results need a stronger relevance check. Do not use it when: Do not use a reranker on an unlimited collection or let top-k scores hide missing candidates. AI requirements: Define the input and source evidence. Set model and tool permissions. Use versioned evaluation data and measures. Define failure, decline-to-answer, privacy, speed, and cost limits. Operational safeguards: Keep a clear trace and hide sensitive log data. Show users a safe fallback. Mark steps that need human review. Define how to roll back the model or prompt. Acceptance criteria: Record baseline and target measures on a versioned evaluation set. Test edge cases and hostile inputs. Verify fallback, monitoring, permissions, and rollback. Evidence and limits (evidence boundary): No single official standard controls the whole term. Its meaning may change across teams, platforms, or frameworks. Unknowns to confirm: Target task, model and version, evaluation owner, source data, risk limit, tool permissions, and production fallback.
Check this request
Review the current use of Reranking. Definition: Reranking uses a second model to score search results again. It compares the question and each found document more closely, then changes their order. Release checks: Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback. Before changing code, report the evidence you found, gaps, severity, and the smallest safe fix.