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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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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?→
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