Phoenix Evals

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

Quick Reference

Task Files

Setup setup-python, setup-typescript

Decide what to evaluate evaluators-overview

Choose a judge model fundamentals-model-selection

Use pre-built evaluators evaluators-pre-built

Build code evaluator evaluators-code-python, evaluators-code-typescript

Build LLM evaluator evaluators-llm-python, evaluators-llm-typescript, evaluators-custom-templates

Batch evaluate DataFrame evaluate-dataframe-python

Run experiment experiments-running-python, experiments-running-typescript

Create dataset experiments-datasets-python, experiments-datasets-typescript

Generate synthetic data experiments-synthetic-python, experiments-synthetic-typescript

Validate evaluator accuracy validation, validation-evaluators-python, validation-evaluators-typescript

Sample traces for review observe-sampling-python, observe-sampling-typescript

Analyze errors error-analysis, error-analysis-multi-turn, axial-coding

RAG evals evaluators-rag

Avoid common mistakes common-mistakes-python, fundamentals-anti-patterns

Production production-overview, production-guardrails, production-continuous

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)

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

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