Debug AI Output by Making It Explain Every Decision

The article introduces the 'Rubber Duck Prompt' - a technique to debug AI-generated code by forcing the AI to explain its decision-making process, which can reveal hidden assumptions and edge cases.

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When an AI assistant generates code, it may produce output that appears correct but has underlying issues. The article introduces the 'Rubber Duck Prompt' - a set of 5 questions that force the AI to justify every decision in its implementation. By explaining the reasoning behind its choices, the AI reveals assumptions and edge cases it may have overlooked. This technique works because language models like GPT are pattern-matching from training data, not truly understanding the code. The Rubber Duck Prompt exposes gaps in the AI's reasoning that can indicate bugs, such as incorrect assumptions about input data or failure to consider certain scenarios. The author provides a real example where the Prompt caught a race condition issue in a rate limiter implementation that the AI had not considered. The article recommends using this technique after any substantial AI-generated code, before merging AI-written PRs, and when onboarding to unfamiliar AI-authored code.