AI Dev Skills
Evals & Benchmarking
✗ Missing — critical gap
What is it?
Systematic testing of LLM outputs for quality, accuracy, and safety — run automatically in CI pipelines, just like unit tests. Evals catch regressions before they reach users.
Why it matters for AI PMs
Prevents quality regressions when models or prompts change. Without evals, every model upgrade is a gamble. Enterprise AI deployment increasingly requires documented eval results.
The 2026 landscape
DeepEval and RAGAS are the community standards. Running evals in CI is now table stakes for production AI teams. The OpenAI evals framework popularized the approach.
What strong coverage looks like
4+ eval repos indicates an engineering culture that treats AI quality like software quality. They catch regressions automatically and can confidently ship model upgrades.
Your library coverage (0 repos)
No repos in this skill area yet.
Key concepts to know
- •LLM-as-judge evaluation
- •RAG metrics: faithfulness, relevance, context recall
- •Red teaming and adversarial testing
- •Benchmark suites (MMLU, HumanEval)
- •Regression detection in CI