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# Why evals are essential for AI product managers

**[LogRocket](https://daily.dev/sources/logrocket)** · 13 min read · 0 upvotes · 0 comments

## Summary

AI product teams often ship features without a systematic way to measure output quality, leading to a cycle of guessing and patching. Evals (evaluations) are the structured alternative: define what good looks like, rate a sample of outputs against explicit pass/fail criteria, identify failure modes, and trace them back through the pipeline to their root cause. Using a real-world AI audio news aggregator as a case study, the post walks through building a minimum viable eval process — starting with a spreadsheet and 30 samples — then scaling to an LLM-as-a-judge setup calibrated against human verdicts. Key lessons include using binary pass/fail criteria over numeric scales, targeting 80%+ agreement between human and automated judge, keeping humans in the loop for spot checks, and treating eval results as direct product roadmap inputs rather than background metrics.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://blog.logrocket.com/product-management/ai-evals-product-quality>

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---

Tags: [#llm](https://daily.dev/tags/llm), [#product-management](https://daily.dev/tags/product-management), [#prompt-engineering](https://daily.dev/tags/prompt-engineering)

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