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Metadata

As explained in The Art of AI Product Development

Metadata is structured descriptive information about a document, such as its author, date, type, or keywords.

Metadata is structured descriptive information about a document, such as its author, date, type, or keywords. In search and retrieval-augmented generation systems, it is one lever for improving how accurately and relevantly documents are found. section 7.2.4

Metadata sits alongside other search-system optimization choices:

  • Document chunking determines how content is divided for retrieval. section 7.2.4
  • Embedding models and retrieval methods affect how relevant content is identified. section 7.2.4
  • Metadata contributes to the broader effort to deliver accurate, relevant results to users. section 7.2.4

How The Art of AI Product Development covers it

How deep it goes
Identifies metadata as an optimization input but does not explain its implementation or retrieval mechanics.
Examples
Names articles, reports, research papers, how-to guides, case studies, industry analyses, and annual reports as document types.
What you can do after
Configure a search system with metadata to improve the accuracy and relevance of retrieved results.
How it connects
Connects metadata with document chunking, embedding models, retrieval methods, 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.