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Core
Late-Interaction Retrieval (ColBERT)
Late-interaction retrieval represents each document as one vector per token rather than a single pooled vector, then scores a query by summing the best token-to-token matches (MaxSim). It sits between cheap single-vector bi-encoders and expensive cross-encoder rerankers: more precise than a single vector, far cheaper than running a full reranker on every candidate, but with a large storage cost. Applied AI interviews probe it because knowing when this middle tier is worth its disk footprint shows real retrieval-architecture judgment.
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RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
RAG & Agent System DesignDesign a production RAG system over 10M documents serving ~1,000 QPS at sub-second latency.→RAG & Agent System DesignHow do you choose chunk size and decide between dense, sparse (BM25), and hybrid retrieval?→RAG & Agent System DesignHow do you operate a multi-vector (ColBERT-style) index in production without it blowing up storage?→System Design for AI in ProductionDesign an enterprise semantic search system over a company's internal documents and tools.→LLM & GenAI FundamentalsWhen do you choose prompting vs RAG vs fine-tuning for a customer problem?→RAG & Agent System DesignHow do you evaluate a RAG system end to end when you have no single ground-truth answer?→
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