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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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COURSES COVERING THIS TOPIC
No lesson covers this one directly yet. These teach the surrounding topic from the beginning.
Agent Engineering·Agents in the interviewPremium12mThe sentences that mark someone who has run oneThe final lesson. Six specific things a candidate says that could only come from operating an agent rather than building one, and the three claims that reliably mark the opposite.Applied AI Engineering·The interviewPremium12mTurning this course into a study planA concrete four-week plan mapping the seven modules onto the question bank, plus what to do differently if your interview is next week rather than next month.
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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