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Why You Need Evals When Building with AI (LLM Output Is Unpredictable)

Agents & MCP◆ Intermediate◆ Advanced@carol.monroesaved JUN 11

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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.

✦ Walkthrough+10 xp / step

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.

0 / 5 steps · adopt for +40

$ suggested_prompt+40xp

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.

Why You Need Evals When Building with AI (LLM Output Is Unpredictable)

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