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Debugging

As explained in The AI Pocket Book

The process of diagnosing and fixing software errors or incorrect behavior.

Debugging is the practical work of finding and fixing code that behaves incorrectly. It matters especially when generated code looks convincing but rests on a wrong assumption. section 5.5

A weather map that fails

An engineer asked ChatGPT to add Singapore weather stations to an existing map. The code failed because it assumed the Weather API returned a different data format. Making the prompt specify the actual JSON structure fixed that problem. A wrong CDN URL later showed that debugging remains necessary even after prompts become more detailed. section 5.5

Effective debugging here means:

  • Check whether the implementation matches the external API rather than trusting plausible-looking code. section 5.5
  • Improve the prompt when an incorrect assumption caused the failure, but continue validating the result. section 5.5
  • Treat AI-supplied fixes as leads, not verdicts; confident wrong pointers can create an endless rabbit hole. section 5.5

How The AI Pocket Book covers it

How deep it goes
Demonstrates assumption checking and prompt revision through an API failure, but stops short of a general debugging procedure.
Examples
Uses a weather-map integration, JSON response-format mismatch, and incorrect CDN URL as debugging examples.
What it assumes
Assumes enough software and API familiarity to compare generated code with returned data and identify incorrect URLs.
What you can do after
Diagnose/debug with it by checking assumptions, refining prompts, and validating proposed fixes against actual behavior.
How it connects
Connects debugging with software validation, APIs, prompting, and hallucination.

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Related concepts

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