Individual skill
ai-evals
Help users build robust infrastructure for measuring, monitoring, and iterating on AI product performance using human, code-based, and LLM-as-a-judge methodologies.
skills/ai-evals/SKILL.md
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- ai-agents Repository topic
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SKILL.mdcore · 6,636 bytesreferences/artifacts.mddoc · 46,970 bytesreferences/guest-insights.mddoc · 29,823 bytes
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# AI Evaluation Strategy
Move beyond vibe checks to systematic, empirical measurement of AI product quality and reliability.
Help the user with ai evaluation strategy using insights from 11 guests and posts across Lenny's Podcast and Newsletter.
## How to Help
1. **Identify Failure Modes** - Help the user conduct error analysis on real traces to find where the system specifically breaks.
2. **Select Eval Methods** - Recommend the right mix of human, code, and LLM judges based on the specific technical use case.
3. **Build Gold Sets** - Assist in curating a reference dataset of high-quality examples to act as the ground truth for your application.
4. **Operationalize** - Guide the user in integrating these evaluations into a CI/CD pipeline for continuous quality improvement.
## Core Principles