liveBook · Manning

Multistage classification approach

As explained in Knowledge Graphs and LLMs in Action

A multistage classification approach divides classification into sequential stages that can be refined independently.

A multistage classification approach divides a complex classification task into sequential decisions. Each stage can use narrower categories and be refined independently, improving control over difficult questions and edge cases. section 14.5.2

A practical routing pipeline can progressively narrow the classification:

  • Refine each prompt with relevant background, boundary cases, examples, instructions, and an expected output format. section 14.5.2

Knowledge-graph question routing

A question about update frequency may be routed first to a broad category and then interpreted as Documentation-Related when it asks about a documented system feature. Ambiguous cases expose where prompts need more context.

How Knowledge Graphs and LLMs in Action covers it

How deep it goes
Explains sequential routing and prompt refinement, but stops short of specifying model architecture or implementation code.
Examples
Uses knowledge-graph questions routed among data, documentation, schema, and feedback categories, including an ambiguous update-frequency query.
What it assumes
Assumes a defined classification task and categories while emphasizing context, boundary cases, examples, and output formats.
What you can do after
Evaluate whether to retain a broad prompt or split classification into independently refinable stages.
How it connects
Connects intent detection with prompt design, knowledge graphs, documentation, schemas, and feedback handling.
Cover of Knowledge Graphs and LLMs in Action
liveBookmanning.com
Read Knowledge Graphs and LLMs in Action on liveBook Get the book at manning.com

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Knowledge Graphs and LLMs in Action. The section links point to the parts of the book it draws on.