Phoenix EvalsCAUTION

AI Observability & Evaluation

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: phoenix-evals
description: Build, run, and validate evaluators for AI/LLM applications with Phoenix, in Python or TypeScript (including Vercel AI SDK apps) — code-based checks and LLM-as-a-judge, run over exported traces or dataframes or as Phoenix experiments, gate them in CI, and validate judges against human-labeled data.
license: Apache-2.0
compatibility: Requires Phoenix server. Python skills need phoenix and openai packages; TypeScript skills need @arizeai/phoenix-client.
metadata:
  author: [email protected]
  version: "1.0.0"
  languages: "Python, TypeScript"
---

# Phoenix Evals

Build evaluators for AI/LLM applications. Code first, LLM for nuance, validate against humans.

## Quick Reference

| Task | Files |
| ---- | ----- |
| Setup | [setup-python](references/setup-python.md), [setup-typescript](references/setup-typescript.md) |
| Decide what to evaluate | [evaluators-overview](references/evaluators-overview.md) |
| Choose a judge model | [fundamentals-model-selection](references/fundamentals-model-selection.md) |
| Use pre-built evaluators | [evaluators-pre-built](references/evaluators-pre-built.md) |
| Build code evaluator | [evaluators-code-python](references/evaluators-code-python.md), [evaluators-code-typescript](references/evaluators-code-typescript.md) |
| Build LLM evaluator | [evaluators-llm-python](references/evaluators-llm-python.md), [evaluators-llm-typescript](references/evaluators-llm-typescript.md), [evaluators-custom-templates](references/evaluators-custom-templates.md) |
| Batch evaluate DataFrame | [evaluate-dataframe-python](references/evaluate-dataframe-python.md) |
| Run experiment | [experiments-running-python](references/experiments-running-python.md), [experiments-running-typescript](references/experiments-running-typescript.md) |
| Run evals in a test runner (CI gate) | [integrations-pytest](references/integrations-pytest.md), [integrations-vitest-jest](references/integrations-vitest-jest.md) |
| Create dataset | [experiments-datasets-python](references/experiments-datasets-python.md), [experiments-datasets-typescript](references/experiments-datasets-typescript.md) |
| Generate synthetic data | [experiments-synthetic-python](references/experiments-synthetic-python.md), [experiments-synthetic-typescript](references/experiments-synthetic-typescript.md) |
| Validate evaluator accuracy | [validation](references/validation.md), [validation-evaluators-python](references/validation-evaluators-python.md), [validation-evaluators-typescript](references/validation-evaluators-typescript.md) |
| Export spans | [observe-tracing-setup](references/observe-tracing-setup.md) |
| Write a span filter (`SpanQuery().where`) | [filter-expressions](references/filter-expressions.md) |
| Sample traces for review | [observe-sampling-python](references/observe-sampling-python.md), [observe-sampling-typescript](references/observe-sampling-typescript.md) |
| Analyze errors | [error-analysis](references/error-analysis.md), [error-analysis-multi-turn](references/error-analysis-multi-turn.md), [axial-coding](references/axial-coding.md) |
| RAG evals | [evaluators-rag](references/evaluators-rag.md) |
| Avoid common mistakes | [common-mistakes-python](references/common-mistakes-python.md), [fundamentals-anti-patterns](references/fundamentals-anti-patterns.md) |
| Production | [production-overview](references/production-overview.md), [production-guardrails](references/production-guardrails.md), [production-continuous](references/production-continuous.md) |

## Workflows

**Starting Fresh:**
[observe-tracing-setup](references/observe-tracing-setup.md) → [error-analysis](references/error-analysis.md) → [axial-coding](references/axial-coding.md) → [evaluators-overview](references/evaluators-overview.md)

**Building Evaluator:**
[fundamentals](references/fundamentals.md) → [common-mistakes-python](references/common-mistakes-python.md) → evaluators-{code|llm}-{python|typescript} → validation-evaluators-{python|typescript}

**RAG Systems:**
[evaluators-rag](references/evaluators-rag.md) → evaluators-code-* (retrieval) → evaluators-llm-* (faithfulness)

**Gating CI:**
evaluators-{code|llm}-{python|typescript} → integrations-{pytest|vitest-jest} → [production-continuous](references/production-continuous.md)

**Production:**
[production-overview](references/production-overview.md) → [production-guardrails](references/production-guardrails.md) → [production-continuous](references/production-continuous.md)

## 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% accuracy, >70% 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 |

CAUTIONgrade B · trust 89/100 Install with care. The audit found things worth knowing before you trust its output.

What the source does

Filesystem

none-observed

Network

none-observed

Shell

none-observed

Dependencies

pinned

Secrets in source

none-found

Findings (5)

.claude/skills

.codex/skills

.cursor/skills/mintlify

.cursor/skills/vercel-react-best-practices

CLAUDE.md

Gates applied: no_behavioural_pass.

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