Search & IR · advanced
Reranking
A second-stage cross-encoder pass that reorders the top-k candidates.
Mental model
Retrieval is recall-oriented and cheap; reranking is precision-oriented and expensive. Retrieve ~100 candidates fast, then run a heavier cross-encoder on just those to reorder the top 10.
How to study Reranking
Begin by restating the mental model in your own words, then connect it to a concrete system you have built or operated. Name the mechanism, the constraint it addresses, and the trade-off it introduces. Use Sentence-Transformers — Cross-Encoders to check details, but close the source before writing your explanation. Retrieval is the learning step; rereading is only preparation.
Next, compare Reranking with Hybrid Search, RAG, Search Evals. Ask what changes in correctness, latency, resource use, operability, and failure recovery. Complete Cross-encoder rerank top-k and preserve the command, input, output, and one failed attempt as evidence. Finish by explaining the idea without jargon to someone who has not studied the track.
Proof of understanding
- Explain the mechanism from first principles and identify the state it reads or changes.
- Give one situation where the concept is the right choice and one where it is not.
- Predict a realistic failure mode before running the drill, then compare the prediction with evidence.
- Connect the result to a roadmap or build artifact instead of treating the concept as isolated trivia.
Where it matters
Cohere Rerank, cross-encoder rerankers in RAG pipelines.
Common mistakes
- Reranking the whole corpus instead of a candidate set
- Adding a reranker without measuring latency budget
- Not checking that reranking actually beats the retriever on evals
Learn from primary sources
Practice and explain it back
Cross-encoder rerank top-k
Retrieve top-5 by BM25, then rerank with scores [0.9,0.2,0.8,0.1,0.7] for query "payment API". Return new order.
Expected evidence: Doc0, Doc2, Doc4, Doc1, Doc3.
Open the interactive drill →Review prompts
- Why is reranking done as a second stage instead of over the whole corpus?
Build evidence
Use a roadmap capstone to turn this concept into working evidence.