Data & AI

Fix RAG Recall with Contextual Retrieval

Data & AI Pipelines diagram · made in Skemio

Contextual Retrieval Architecture showing raw documents split into chunks, enriched with context by an LLM, and indexed into dense vector and BM25 stores for hybrid query retrieval.
Contextual Retrieval Architecture showing raw documents split into chunks, enriched with context by an LLM, and indexed into dense vector and BM25 stores for hybrid query retrieval.
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What it shows

  • LLMs prepend 50-100 token context headers to each chunk before indexing.
  • Reduces retrieval failure rates by up to 49% when paired with reranking.
  • One-time ingestion cost eliminates ambiguous chunk matches forever.

The takeaway

Prepend document-level context to chunks before embedding to stop silent RAG failures.

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