What Is Reranking? A Second, More Careful Pass Over Search Results
Initial retrieval is built for speed across a huge dataset — reranking is a slower, more careful second pass that reorders a shortlist for the best possible accuracy.
A second, more careful look
Reranking takes an initial set of retrieved candidates — often dozens, from a fast but comparatively coarse first search pass — and re-scores them with a slower, more precise model that looks more carefully at each candidate's relevance, before returning only the very best few.
Why not just use the more precise method from the start
The more accurate models used for reranking are too slow to run against an entire dataset of millions of documents directly. Running a fast, approximate search first to narrow the field, then applying a slower but more accurate reranker only to that shortlist, gets the benefit of precision without the prohibitive cost.
Where it fits in a retrieval pipeline
In a typical RAG pipeline, reranking sits between initial retrieval (fast, broad, from semantic or hybrid search) and the final step of handing content to the model — it's specifically the step that trades some speed for meaningfully better relevance.
Frequently Asked Questions
What is reranking in a search or RAG pipeline?
A second-pass step that re-scores an initial set of retrieved candidates using a slower, more precise model, narrowing them down to the most relevant few before they're used further.
Why not always use the more precise reranking model for the whole search?
It's too computationally expensive to run against an entire large dataset. Running a fast, approximate search first to narrow the field makes applying the precise reranker to just that shortlist practical.