Reranking Retrieved Results
Add a reranking stage that puts the truly relevant passages first, within your latency and cost budget.
A taste of a lesson
We added a cross encoder reranker but answers barely improved. What should I check?
First check whether the right passages were in the candidate set at all. Take 30 labelled queries and measure recall at N for the first stage, where N is how many candidates you rerank. If the relevant passage is missing for many queries, the reranker has nothing to promote, and the fix is upstream: chunking, hybrid search or a larger N. If recall at N is good but answers are still weak, look at what reaches the generator. What N are you using now?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Explain two stage retrieval and why cross encoders rank more accurately
- Check first stage recall before adding a reranker
- Compare cross encoder and language model rerankers on cost and quality
- Set candidate counts and calibrated score thresholds
- Measure ranking quality and latency before and after reranking
Lesson plan
- 1 The two stage pattern Understand fast candidate retrieval followed by accurate reordering. Start
- 2 Why rerankers are more accurate Compare bi encoders and cross encoders by what they see. Start
- 3 Check first stage recall Confirm the relevant passages are among the candidates before reranking. Start
- 4 Choosing a reranker Weigh cross encoders against language model rerankers. Start
- 5 Candidates, thresholds and latency Tune candidate count and cut offs within your budget. Start
- 6 Measuring the effect Show reranking gains with ranking metrics and end to end checks. Start
Try asking
About this tutor
For developers with a working retrieval system whose top results are often relevant but in the wrong order, or diluted by near misses. You learn the two stage pattern: a fast first stage retrieves a broad candidate set and a slower, more accurate reranker reorders it. You compare cross encoder rerankers with using a language model as a reranker, set candidate counts and cut off thresholds, and fit reranking into a latency budget. Evaluation is central: you measure ranking quality before and after, and you learn why reranking cannot rescue passages the first stage never retrieved.
Reviews
4.5
2 ratingsSample
- Svetlana I.Sample
We measured first stage recall and found the reranker was not the problem at all. Fixing chunking first and then reranking gave a clear jump in nDCG.
- Andre B.Sample
The explanation of why cross encoders catch negation was excellent. Threshold calibration section was short but practical.
About the teacher
Search engineer teaching embeddings, chunking, vector and keyword search, and reranking from first principles
9 tutors 374 lessons taught Sample
I come from search: indexes, ranking and the long tail of queries that make a search box look foolish. When retrieval augmented generation arrived, most of what mattered turned out to be old search problems in new clothes, so that is how I teach it. We start with how text becomes something you can compare, then how documents are split,...
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