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Core
Reranking
Reranking is a two-stage retrieval design: a fast bi-encoder fetches a broad candidate set for recall, then a slower but more accurate cross-encoder rescoring each (query, document) pair reorders them for precision. The cross-encoder is better because it reads query and document together rather than as precomputed vectors. Reranking lets you feed fewer, better chunks to the model, often the highest-ROI improvement to a RAG system. Applied-AI interviews probe it because it is the cheapest large win in retrieval quality.
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TAUGHT IN THESE COURSES
This concept is a reference. These lessons put it in sequence, with what comes before and after it.
Applied AI Engineering·RetrievalFree14mWhy your search misses the obvious answerPure vector search fails in two specific, predictable ways, and both have standard fixes that most first systems are missing. This lesson covers the two-stage design that turns a demo into something that finds the right passage.Applied AI Engineering·RetrievalFree240mProject: build a retrieval system over your own documentsThe first project of the course. Build a working question-answering system over documents you actually have, in five stages, each one checkable on its own, and finish with something honest enough to put in a portfolio.
RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
RAG & Agent System DesignWhat is reranking in a RAG pipeline, and why does a cross-encoder reranker improve results?→RAG & Agent System DesignDesign a production RAG system over 10M documents serving ~1,000 QPS at sub-second latency.→RAG & Agent System DesignWhat is late interaction (ColBERT), and how does it sit between bi-encoders and cross-encoders?→RAG & Agent System DesignYour vector search returns high similarity scores but irrelevant results. How do you debug it?→RAG & Agent System DesignHow do you choose chunk size and decide between dense, sparse (BM25), and hybrid retrieval?→RAG & Agent System DesignContext windows are now huge. When do you just stuff everything in context instead of building RAG?→
COMPANIES THAT ASSUME THIS