Use Free AI Model Tokens for Debugging, Not Code Generation, Dev Argues

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A developer writing on DEV Community argues that free AI model tokens are better spent on debugging assistance than on code generation. The core reasoning is that debugging consumes disproportionate developer time through pattern-matching tasks that AI models are well-suited to handle, having been trained on thousands of similar error patterns. While generated code still requires review, testing, and integration, a correct diagnosis immediately unblocks a developer. The author outlines a five-step workflow — collecting logs, extracting error blocks, building a prompt, calling an OpenAI-compatible endpoint, and verifying the top hypothesis — implemented in a minimal Python script. The piece concludes that halving bug-resolution time delivers more practical value than generating functions that still need to be validated and shipped.

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