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Phoenix Evals
phoenix-evals
Build and run evaluators for AI/LLM applications using Phoenix.
SKILL.md
Full skill instructions
Phoenix Evals
Build evaluators for AI/LLM applications. Code first, LLM for nuance, validate against humans.
Quick Reference
Workflows
Starting Fresh: observe-tracing-setup → error-analysis → axial-coding → evaluators-overview
Building Evaluator: fundamentals → common-mistakes-python → evaluators-{code|llm}-{python|typescript} → validation-evaluators-{python|typescript}
RAG Systems: evaluators-rag → evaluators-code-* (retrieval) → evaluators-llm-* (faithfulness)
Gating CI: evaluators-{code|llm}-{python|typescript} → integrations-{pytest|vitest-jest} → production-continuous
Production: production-overview → production-guardrails → production-continuous
Reference Categories
| Prefix | Description |
|---|---|
fundamentals-* | Types, scores, anti-patterns |
observe-* | Tracing, sampling |
error-analysis-* | Finding failures |
axial-coding-* | Categorizing failures |
evaluators-* | Code, LLM, RAG evaluators |
experiments-* | Datasets, running experiments |
integrations-* | Run evals from test runners (pytest, Vitest, Jest) as a CI gate |
validation-* | Validating evaluator accuracy against human labels |
production-* | CI/CD, monitoring |
Key Principles
| Principle | Action |
|---|---|
| Error analysis first | Can't automate what you haven't observed |
| Custom > generic | Build from your failures |
| Code first | Deterministic before LLM |
| Validate judges | >80% TPR/TNR |
| Binary > Likert | Pass/fail, not 1-5 |
| Invariants gate, signals trend | assert/expect hard invariants (CI red); log LLM-judge quality signals and gate the aggregate (acceptance criteria), not every case |
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