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Rule-based filter

As explained in Natural Language Processing in Action, Second Edition

A deterministic filtering mechanism that accepts, rejects, or routes inputs or outputs according to explicit rules.

A rule-based filter applies explicit rules to accept, reject, or route inputs or outputs. In conversational systems, it acts as a guardrail around otherwise open-ended responses. section 12.4

Rules provide useful control but cannot capture everything people do in conversation. Production chatbots therefore commonly combine filters with large language models and retrieval-augmented generation to reduce misleading or harmful responses. section 12.4

Guarding a chat product

A chat product can route user inputs or generated outputs through explicit filters before or alongside retrieval-augmented generation and large language model responses. section 12.4

How Natural Language Processing in Action, Second Edition covers it

How deep it goes
Defines the safeguard role and coverage limitation but stops short of implementation details, rule syntax, and testing procedures.
Examples
Uses chat products and user-facing responses as contexts for preventing misleading or harmful outputs.
What you can do after
Operate/configure a hybrid chatbot by combining explicit filters with retrieval-augmented generation and language-model responses.
How it connects
Connects rule-based filters with large language models, retrieval-augmented generation, hybrid chatbots, and search.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Natural Language Processing in Action, Second Edition. The section links point to the parts of the book it draws on.