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Contextual retrieval

As explained in The Art of AI Product Development

Contextual retrieval enriches retrieved text segments with surrounding document information before embedding and indexing.

Contextual retrieval reduces the ambiguity created when documents are divided into chunks. Before embedding and indexing, it prepends each chunk with concise background drawn from the surrounding document, making retrieved text more precise and useful for response generation. section 7.2.4

Making a fragment meaningful

A standalone result saying “The platform reduced processing time by 30%” leaves the platform, process, and date unclear. Contextualization can turn it into: “This case study describes how a logistics company used the platform to reduce invoice processing time by 30% in Q4 2023.” An LM can generate a 50–100-token summary for each chunk automatically, avoiding manual annotation at scale. section 7.2.4

How The Art of AI Product Development covers it

How deep it goes
Explains contextual prepending, automated summary generation, token length, and a 35% reduction in retrieval failures.
Examples
Uses a logistics case study, an ambiguous processing-time fragment, and automated 50–100-token chunk summaries.
What it assumes
Assumes familiarity with chunking, semantic search, embeddings, indexing, and retrieval-augmented generation.
What you can do after
Build it from scratch by prompting an LM to summarize each chunk’s surrounding document context before embedding and indexing.
How it connects
Connects chunking with embeddings, indexing, semantic search, and retrieval-augmented generation.
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This explainer was generated by Manning's knowledge graph from the text of The Art of AI Product Development. The section links point to the parts of the book it draws on.