Contextual embedding beyond the gold passage
A new training method, model, and benchmark for retrieving answers and their supporting context.
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AI Research Blog Post
Summary
The article presents Contextual Retrieval, an improved RAG technique that preserves contextual signals during embedding and BM25 search, cutting failed retrievals by up to 67% with reranking. It also discusses prompt‑caching for Claude and when a full‑prompt approach is feasible.
Highlights
- Traditional RAG loses context, causing irrelevant or missed results.
- Contextual Retrieval uses Contextual Embeddings and Contextual BM25 to boost accuracy.
- Failed retrievals drop 49% overall and 67% when combined with reranking.
- Claude deployment instructions are provided via a cookbook.
- Prompt caching now makes full‑prompt methods faster and up to 90% cheaper.
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Context
Audience
Developers and researchers building AI systems that need reliable knowledge retrieval for customer support, legal analysis, or other domain‑specific assistants
DomainMachine Learning
Formatlong-form article
Accessfree online
Topics
Related
Retrieval‑Augmented GenerationVector database searchClaude prompt cachingBM25 algorithmEmbedding models
