Core
Reranking and Two-Stage Retrieval
Two-stage retrieval pairs a cheap recall-heavy first stage that pulls dozens of candidates with a precise reranker that re-scores each one against the query. It is the standard fix when vector search returns relevant-ish chunks but the right one is not in the top few, and it trades a little latency for a lot of precision.
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RAG & Agent System DesignYou added a cross-encoder reranker. p95 latency roughly doubled and answer quality did not move. Explain what happened and fix it. →hard · PremiumRAG & Agent System DesignWalk me through a RAG pipeline end-to-end, and tell me where it usually breaks →easy · FreeRAG & Agent System DesignHow would you chunk documents for retrieval, and how do you know your chunking is good? →easy · FreeLLM & GenAI FundamentalsYour prompt plus retrieved documents exceed the context window. What are your options and tradeoffs? →medium · Free with sign-inLLM & GenAI FundamentalsWhen do you use long context, RAG, or prompt caching, and what are the failure modes of each? →hard · PremiumRAG & Agent System DesignA chunking change shipped last week. Aggregate recall barely moved, and support says answers got worse. Find the regression. →hard · Premium