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Technical implementation · AI Search Infrastructure

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Definition

A retrieval pipeline is the sequence of steps an AI system takes to find, rank, and return relevant content in response to a query — including query embedding, vector search, re-ranking, and passage extraction before final answer synthesis. The retrieval pipeline is the path content must travel to become a citation. Content that is indexed, properly embedded, semantically relevant to the query, and structured for extraction has a chance to complete the pipeline. Content that fails at any step — not indexed, poorly embedded, semantically vague, or structurally opaque — drops out. Understanding the pipeline helps prioritize which optimization efforts matter most: technical indexability first, semantic relevance second, structural extractability third.

RAG

Dense retrieval

Vector database

Chunking

Re-ranking

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