Contextual retrieval
Contextual retrieval prepends a chunk's surrounding context (document title, section, summary) to each chunk before embedding, dramatically improving retrieval relevance on long documents.
Introduced by Anthropic in 2024 and now standard in 2026 RAG stacks, contextual retrieval addresses a core RAG failure: a chunk that says "the company recorded record revenue" is ambiguous without knowing which company and which quarter. The fix: prepend a model-generated summary of the chunk's surrounding context ("From Acme Corp's Q3 2025 earnings call: ...") before embedding. Empirically reduces retrieval failure rates by 35-67% on long-document corpora vs naive chunking. Pairs naturally with hybrid (vector + BM25) and re-ranking for state-of-the-art retrieval in 2026.
When to use contextual retrieval
- RAG over long structured documents (earnings calls, legal filings, books).
- Multi-document corpora where chunks lose meaning without context.
Common mistakes
- Generating context with a weak model — degrades the signal.
- Forgetting that re-embedding the corpus is required when the contextualisation prompt changes.
FAQ
What is contextual retrieval?
Contextual retrieval prepends a chunk's surrounding context (document title, section, summary) to each chunk before embedding, dramatically improving retrieval relevance on long documents.
When should I use contextual retrieval?
RAG over long structured documents (earnings calls, legal filings, books). Multi-document corpora where chunks lose meaning without context.
What are the most common mistakes with contextual retrieval?
Generating context with a weak model — degrades the signal. Forgetting that re-embedding the corpus is required when the contextualisation prompt changes.
Related terms
- Retrieval-augmented generation (RAG) — Retrieval-augmented generation (RAG) injects relevant documents into the prompt at query time so the model answers from your data instead of its training memory.
- Embeddings — Embeddings are dense numeric vectors that represent the meaning of text, images, or other data, allowing similarity to be measured as vector distance.
- Semantic search — Semantic search finds documents by meaning rather than keyword match, using embedding similarity in a vector space.
- Grounding — Grounding is any technique that ties a language model's output to verifiable sources — retrieved documents, tool results, structured data — instead of pure memory.
Sources
Last updated: 2026-06-01. Raw markdown: https://promtable.com/glossary/contextual-retrieval.md.