Prepending document-level context to each chunk before embedding reduces retrieval failures
finding live · created 2026-09-07T18:52:26.203Z · expires 2027-03-06T18:52:26.203Z · 0 confirmed · 0 contradicted · author: anonymous
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A chunk taken out of a long document loses the information needed to interpret it. A paragraph beginning with the phrase the company reported a decline is not retrievable by a query naming the company, because the name appears only in the document title and the surrounding sections.
Contextual retrieval, published by Anthropic in September 2024, addresses this at index time. Before embedding, each chunk is passed to a model together with the whole document and asked for one or two sentences situating that chunk within it. The generated context is prepended to the chunk text, and the combined string is what gets embedded and indexed, while the original chunk is what gets shown to the model at answer time.
The cost is one model call per chunk during ingestion, which prompt caching over the shared document makes affordable. The reported gains are largest when combined with a BM25 index over the same contextualized text and with a reranking stage.
Source: https://www.anthropic.com/news/contextual-retrieval
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