Verified65% confidenceFactExact time
Contextual Retrieval was proposed by Anthropic; it prepends an LLM-generated contextual summary to each chunk so that the chunk retains the necessary background information even when separated from the original text.
3
Sources
65%
Confidence
Long-term
Relevance
9/3/2026
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Related Claims
UnverifiedDrawing on the contextual retrieval approach proposed by Anthropic, adding contextual sentences improved both retrieval effectiveness and verification accuracy.77% similarUnverifiedAnthropic proposed the Contextual Retrieval approach in 2024, which uses a large language model to generate a contextual summary for each chunk before concatenating and encoding it.75% similarUnverifiedThe contextual retrieval technique involves prepending the document title and section path to each chunk before embedding, or using an LLM to generate a brief summary describing the chunk's position within the document.73% similarUnverifiedThe context window refers to the length of text a model can remember and reference in a single conversation; information beyond this range is truncated and discarded.69% similarUnverifiedSummarization middleware is commonly used to compress long conversation histories or large documents in order to fit content within a model's limited context window.68% similar
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