Passage retrieval is the retrieval task of finding the most relevant short text spans rather than whole documents for answering a query - it is central to RAG because generation quality depends on precise, context-sized evidence.

What Is Passage retrieval?

• Definition: Search process that ranks small chunks or passages by query relevance.
• Granularity Goal: Return evidence units that fit model context limits and preserve answer-bearing detail.
• Index Unit: Typically uses chunked passages with metadata linking back to source documents.
• Pipeline Role: First critical step before reranking and grounded generation.

Why Passage retrieval Matters

• Context Efficiency: Sending full documents wastes tokens and dilutes answer signal.
• Accuracy Impact: Correct passage selection strongly determines factual answer quality.
• Latency Control: Smaller units improve retrieval speed and downstream processing efficiency.
• Hallucination Reduction: Targeted evidence lowers unsupported generation risk.
• Auditability: Passage-level evidence supports precise citation and verification.

How It Is Used in Practice

• Chunked Corpus Build: Split documents into indexed passages with source and position metadata.
• Two-Stage Ranking: Use fast retrieval followed by reranking for high-precision top-k.
• Answer Attribution: Carry passage IDs into generation for evidence-linked outputs.

Passage retrieval is the evidence-selection core of modern RAG systems - high-quality passage ranking is required for factual, efficient, and verifiable AI responses.