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Chunking

As explained in Financial AI in Practice

Chunking divides documents or other content into smaller segments for processing or retrieval.

Chunking splits documents into smaller segments for retrieval. Small chunks can produce specific matches but lose context; large chunks preserve context but can blur several ideas in one embedding. section 3.3.2

A two-stage strategy searches small chunks, then retrieves the larger parent document for context. Chunking is not automatic: short, self-contained items can remain intact as natural chunks. section 3.3.2 section 11.3.2

Choosing granularity for financial content

A 50,000-word Form 10-K may need splitting. An 80-character headline or 250-character summary is already a self-contained unit; splitting it further can fragment meaning without improving retrieval. section 11.3.2

How Financial AI in Practice covers it

How deep it goes
Explains the small-versus-large chunk tradeoff and parent-document retrieval without detailing a full implementation.
Examples
Compares 50,000-word Form 10-K filings with 250-character summaries and 80-character headlines that remain intact.
What you can do after
Evaluate whether to split content, retain it intact, or retrieve parent documents after searching smaller chunks.
How it connects
Connects chunking to retrieval-augmented generation, embeddings, vector databases, and similarity search.

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This explainer was generated by Manning's knowledge graph from the text of Financial AI in Practice. The section links point to the parts of the book it draws on.