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Code readability

As explained in The Art of AI Product Development

Readability is the degree to which written content can be understood easily by its intended audience.

For language-model responses, readability is an evaluation metric for clarity and accessibility: it asks whether content is easy for its intended users to understand. It complements measures such as accuracy and contextual relevance. section 5.4.2

Assessing readability

Evaluate responses using readability scores or evaluator ratings. These checks can sit within a hybrid evaluation process that combines automated assessment with human review. section 5.4.2

How The Art of AI Product Development covers it

How deep it goes
Defines readability and names scores or evaluator ratings, without detailing how to calculate them.
Examples
Assesses response clarity using readability scores or evaluator ratings.
What you can do after
Evaluate response clarity and accessibility using scores or evaluator ratings.
How it connects
Connects readability with accuracy, contextual relevance, and brand alignment in language-model evaluation.

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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.