Sentence Window Retrieval — AI Glossary

Reference details

Related terms

• Parent Document Retrieval
• Chunk Overlap
• Advanced RAG

How it works

1. Individual sentences are embedded and indexed, so matching happens at the finest granularity available.
2. Each stored sentence keeps its position in the source document, which is what allows the window to be reconstructed later.
3. At query time the top matching sentences are retrieved, giving high precision on the match itself.
4. Each match is then expanded to include a fixed number of sentences either side, so the model reads context rather than a fragment.
5. Overlapping windows from nearby matches must be merged, or the same text enters the prompt several times.

Example: Why the windows have to be merged

Three matches land on sentences 10, 12 and 14 of the same document, each expanded by two sentences either side. The windows are 8-12, 10-14 and 12-16 — together they cover sentences 8 to 16 but, concatenated naively, sentences 10 to 14 appear two or three times each. Merging gives one 9-sentence passage; not merging sends roughly twice the tokens for the same content, and the repetition itself can bias the model toward the duplicated material.

Commonly confused with

The counterpart to parent-document retrieval. Rather than returning an authored unit, it expands a fixed number of sentences either side of the match, so it needs only sentence order and no document structure at all. The trade is that the window can cut across a boundary an author intended, returning half of one idea and half of the next.

When to use it

Reach for it when:

• Matching must be sentence-precise but the model needs surrounding context to answer.
• Documents lack authored units such as sections, so a fixed window is the only expansion available.
• Continuous prose where the relevant span is a few sentences rather than a whole section.

Reach for something else when:

• Documents have real parent units, where returning the authored section is better than a fixed window.
• Windows are not merged, which duplicates text and wastes the budget.
• Sentences are already self-contained, where expansion adds tokens and no information.

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