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73 lines
4.4 KiB
Markdown
73 lines
4.4 KiB
Markdown
---
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name: phoenix-evals
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description: Build and run evaluators for AI/LLM applications using Phoenix.
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license: Apache-2.0
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compatibility: Requires Phoenix server. Python skills need phoenix and openai packages; TypeScript skills need @arizeai/phoenix-client.
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metadata:
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author: oss@arize.com
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version: "1.0.0"
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languages: "Python, TypeScript"
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---
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# Phoenix Evals
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Build evaluators for AI/LLM applications. Code first, LLM for nuance, validate against humans.
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## Quick Reference
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| Task | Files |
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| ---- | ----- |
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| Setup | [setup-python](references/setup-python.md), [setup-typescript](references/setup-typescript.md) |
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| Decide what to evaluate | [evaluators-overview](references/evaluators-overview.md) |
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| Choose a judge model | [fundamentals-model-selection](references/fundamentals-model-selection.md) |
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| Use pre-built evaluators | [evaluators-pre-built](references/evaluators-pre-built.md) |
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| Build code evaluator | [evaluators-code-python](references/evaluators-code-python.md), [evaluators-code-typescript](references/evaluators-code-typescript.md) |
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| 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) |
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| Batch evaluate DataFrame | [evaluate-dataframe-python](references/evaluate-dataframe-python.md) |
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| Run experiment | [experiments-running-python](references/experiments-running-python.md), [experiments-running-typescript](references/experiments-running-typescript.md) |
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| Create dataset | [experiments-datasets-python](references/experiments-datasets-python.md), [experiments-datasets-typescript](references/experiments-datasets-typescript.md) |
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| Generate synthetic data | [experiments-synthetic-python](references/experiments-synthetic-python.md), [experiments-synthetic-typescript](references/experiments-synthetic-typescript.md) |
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| Validate evaluator accuracy | [validation](references/validation.md), [validation-evaluators-python](references/validation-evaluators-python.md), [validation-evaluators-typescript](references/validation-evaluators-typescript.md) |
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| Sample traces for review | [observe-sampling-python](references/observe-sampling-python.md), [observe-sampling-typescript](references/observe-sampling-typescript.md) |
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| Analyze errors | [error-analysis](references/error-analysis.md), [error-analysis-multi-turn](references/error-analysis-multi-turn.md), [axial-coding](references/axial-coding.md) |
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| RAG evals | [evaluators-rag](references/evaluators-rag.md) |
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| Avoid common mistakes | [common-mistakes-python](references/common-mistakes-python.md), [fundamentals-anti-patterns](references/fundamentals-anti-patterns.md) |
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| Production | [production-overview](references/production-overview.md), [production-guardrails](references/production-guardrails.md), [production-continuous](references/production-continuous.md) |
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## Workflows
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**Starting Fresh:**
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[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)
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**Building Evaluator:**
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[fundamentals](references/fundamentals.md) → [common-mistakes-python](references/common-mistakes-python.md) → evaluators-{code|llm}-{python|typescript} → validation-evaluators-{python|typescript}
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**RAG Systems:**
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[evaluators-rag](references/evaluators-rag.md) → evaluators-code-* (retrieval) → evaluators-llm-* (faithfulness)
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**Production:**
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[production-overview](references/production-overview.md) → [production-guardrails](references/production-guardrails.md) → [production-continuous](references/production-continuous.md)
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## Reference Categories
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| Prefix | Description |
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| ------ | ----------- |
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| `fundamentals-*` | Types, scores, anti-patterns |
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| `observe-*` | Tracing, sampling |
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| `error-analysis-*` | Finding failures |
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| `axial-coding-*` | Categorizing failures |
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| `evaluators-*` | Code, LLM, RAG evaluators |
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| `experiments-*` | Datasets, running experiments |
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| `validation-*` | Validating evaluator accuracy against human labels |
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| `production-*` | CI/CD, monitoring |
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## Key Principles
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| Principle | Action |
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| --------- | ------ |
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| Error analysis first | Can't automate what you haven't observed |
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| Custom > generic | Build from your failures |
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| Code first | Deterministic before LLM |
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| Validate judges | >80% TPR/TNR |
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| Binary > Likert | Pass/fail, not 1-5 |
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