liveBook · Manning

Text chunk

As explained in Effective Conversational AI

A coherent segment of text treated as an individual unit for embedding, indexing, or retrieval.

A text chunk is a coherent segment of a document treated as an individual unit for indexing and retrieval. In a retrieval-augmented generation pipeline, chunks are converted into embeddings, stored, and matched against a user’s query to retrieve relevant passages. section 6.3.2

Paragraph-level chunking

A document can be divided at paragraph boundaries, producing smaller units for indexing. Preserving headings, tables, and lists during ingestion can also support search accuracy and relevance. section 6.3.2 section 6.4.3

When preparing documents for retrieval:

  • Treat chunking strategy as a factor that can affect retrieval results. section 6.3.2
  • Partition large documents into representative subdocuments for more granular indexing and retrieval. section 6.4.3

How Effective Conversational AI covers it

How deep it goes
Defines chunks as coherent units and mentions paragraph boundaries, but does not specify how to choose chunk sizes.
Examples
Uses paragraph-boundary splitting and discusses partitioning large documents while retaining headings, tables, and lists.
What you can do after
Evaluate chunking choices as part of preparing documents for more granular retrieval.
How it connects
Connects chunks to embeddings, vector databases, document ingestion, and retrieval-augmented generation.

Also explained in

Compare how 19 books explain text chunk →

Related concepts

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