Debugging with LLMs: Give the Model What It Can't Guess
How to structure debugging sessions with an AI assistant — what runtime context to share, how to frame the problem, and how to verify a diagnosis before patching.
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How to structure debugging sessions with an AI assistant — what runtime context to share, how to frame the problem, and how to verify a diagnosis before patching.
A practical checklist for reviewing AI-written code — catching hallucinated APIs, subtle logic bugs, and security gaps before they reach production.
Five battle-tested prompting patterns — structure, examples, decomposition, self-checking, and output contracts — that hold up when real traffic hits.
A practical decision framework for when to reach for retrieval-augmented generation, when to fine-tune, and when to do neither — with the trade-offs that matter.
A from-scratch walkthrough of the agent loop — tools, reasoning, and termination — using the Claude API and plain TypeScript. No frameworks.