Developer details iterative process to get actionable Go performance tips from LLMs

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A software developer attempted to get specific performance optimization advice for a legacy Go microservice from a Large Language Model. Initial generic prompts yielded only high-level, non-actionable suggestions. The developer then experimented by feeding the model specific code snippets and raw profiling data over several hours. The key breakthrough involved structuring prompts with detailed context, specific data, and a clear goal, treating the LLM as a junior engineer requiring precise guidance.

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