From Error Analysis to Better Prompts: The Missing Step Most Teams Skip

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You've done the error analysis. You've got evals running. You've identified the problems. Now comes the critical question everyone asks: how do you actually improve your system instructions and prompts based on what you found?

The reality: you're working with samples, not analyzing every single trace. You run evals, spot error patterns in your sample data, then make judgment calls about how to fix the underlying issues. But what techniques actually work when you need to update system instructions or refine tool definitions?

This is where theory meets practice in LLM product development. Error identification is just the beginning—the hard part is translating those findings into concrete prompt improvements that work at scale. Different error types require different fixes: some need clearer instructions, others need better examples, some need tool redesigns.

The teams that excel here have developed systematic approaches to going from "here's what's broken" to "here's the specific change that fixes it" without creating new problems elsewhere.

What's your process for turning error analysis into prompt improvements?

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