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Rule of logic

As explained in The Art of AI Product Development

A logical rule is an explicit conditional statement that derives or permits an outcome when specified conditions hold.

A rule of logic is an explicit conditional statement that derives or permits an outcome when specified conditions hold. In rule-based AI, human-made rules encode knowledge and support reasoning without a full model-training cycle. This makes them practical for analytical, relatively simple problems where the relevant domain can be defined in advance. section 3.2.1

Banking compliance

A bank can manually encode rules that verify whether financial transactions satisfy legal and regulatory requirements. The approach can produce predictable, explainable results, accelerate an MVP, and generate training data aligned with user needs. Its coverage remains limited when real-world cases introduce nuances and edge cases. section 3.2.1

How The Art of AI Product Development covers it

How deep it goes
Defines human-authored symbolic rules and their benefits, but stops short of detailed implementation or comprehensive edge-case handling.
Examples
Uses banking transaction compliance, manually dissected problems, prototypes, MVPs, and collected training data as concrete applications.
What it assumes
Assumes an analytical, relatively simple problem whose relevant domain can be defined explicitly by humans.
What you can do after
Apply it via manually coded rules to prototype, test direction, explain outputs, and collect aligned training data.
How it connects
Connects symbolic AI with knowledge representation, databases, ontologies, machine learning, prototypes, and training data.

Related concepts

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.