Why You Need Evals When Building with AI (LLM Output Is Unpredictable)
See more in Agents & MCP →Exact instruction
- Define metrics for what 'good' output looks like for your specific feature.
- Use an evaluation framework to automatically grade model outputs (can use another LLM as a judge).
- Analyze statistical trends in evaluations over time and across many samples.
- Use the framework's sample synthesis feature to generate additional test cases using language models.
- Integrate evaluations directly into your test suite.
Suggested prompt
Set up a simple evaluation for this feature: define what 'correct' output looks like, write 5–10 test cases including edge cases, and create a grading prompt that judges whether the model's output meets the criteria. Run the eval after any prompt change.
Adopt?
Yes: Directly applicable to any Claude Code project with LLM-generated output. Add evals to catch unexpected outputs before users see them. General practice — especially important for features that tag, classify, summarize, or generate structured data.
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Building with AI is exciting, but measuring results is just as important. A few weeks ago, I shared some thoughts on evaluations (evals). Since then, I've continued building products, workflows, applications, and agents, and one thing has become even more clear: building is only half the job. Measuring is the other half. If you develop AI-powered pro...