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term: "Passage retrieval"
canonical: https://citable.wiki/glossary/passage-retrieval
license: CC BY 4.0
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# Passage retrieval

**Definition:** Passage retrieval is the search technique of scoring and returning short spans of text — a paragraph, a section, a window of tokens — rather than whole documents, so that a system can answer a question from the most relevant fragment. It is the retrieval step in most answer engines, and it is why a page is cited passage by passage rather than as a unit.

Passage retrieval comes from open-domain question answering, where the task is to find the few sentences in a large corpus that answer a question. Dense Passage Retrieval ([Karpukhin et al., 2020](https://arxiv.org/abs/2004.04906)) showed that embedding passages and questions into one vector space could beat the classic BM25 keyword baseline; that method, usually combined with keyword matching and a reranker, sits inside most [retrieval-augmented generation](/glossary/retrieval-augmented-generation) pipelines today. Google announced passage-level ranking for ordinary web search at its [Search On event in October 2020](https://blog.google/products-and-platforms/products/search/search-on/).

The practical consequence is that your page is never judged whole. It is cut into [chunks](/glossary/chunking), each is scored against the question, and only the winners reach the model. The heading above a paragraph, its opening sentence and whether it names its subject explicitly decide whether it is a candidate at all.

**Making a passage retrievable**

- [ ] One question per section, with the question or its key phrase in the heading
- [ ] The answer in the first sentence, then the qualification
- [ ] The full entity name in the passage, not a pronoun pointing at an earlier section
- [ ] Figures, units and dates inside the same paragraph as the claim they support
- [ ] Plain HTML paragraphs and lists, so the chunk boundaries fall where the meaning does

Modelling content so that every chunk is self-contained is covered in [Modelling content for answer engines in a headless CMS](/guides/modeling-content-for-answer-engines-in-a-headless-cms).

## Frequently asked questions

### Why does passage retrieval favour answer-first writing?

Because the retriever scores each passage on its own, without the rest of the page. A paragraph that opens with the answer, names the entity in full and needs no earlier context resembles the question closely and is complete when lifted out. A paragraph that builds up to the answer scores lower and, if selected, gives the model a fragment that stops mid-thought.

## Related guides

- [How to write answer-first content that LLMs can quote](https://citable.wiki/guides/how-to-write-answer-first-content)
- [What is Answer Engine Optimization (AEO)?](https://citable.wiki/guides/what-is-answer-engine-optimization)
- [Modelling content for answer engines in a headless CMS!](https://citable.wiki/guides/modeling-content-for-answer-engines-in-a-headless-cms)

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Source: https://citable.wiki/glossary/passage-retrieval