What is the primary benefit of using Phoenix Evals with Claude Code?

It allows you to automate the quality assurance process for AI applications, moving from subjective manual testing to deterministic, code-first evaluation metrics.

Which programming languages are compatible with Phoenix Evals?

The skill provides comprehensive support and templates for both Python and TypeScript environments.

How do I ensure my automated evaluators are accurate?

The skill includes validation workflows that help you compare automated judge scores against human-labeled benchmarks to ensure high precision and recall.

Can I use Phoenix Evals for real-time monitoring?

Yes, it includes production-oriented features for implementing guardrails and continuous monitoring of AI application health.

Does this skill support RAG (Retrieval-Augmented Generation)?

Yes, it includes specialized workflows for evaluating both the retrieval stage and the final response faithfulness in RAG architectures.

Phoenix AI Evaluators

Name: Phoenix AI Evaluators
Author: Arize-ai

byArize-ai

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8,664

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Analytics & Monitoring

Builds, runs, and validates high-performance evaluators for AI and LLM applications using the Phoenix observability framework.

Phoenix AI Evaluators provides a structured framework for improving AI application quality through code-first and LLM-based evaluation. It enables developers to move beyond manual spot-checking by implementing systematic error analysis, RAG-specific metrics, and synthetic data generation. By integrating these tools into your workflow, you can validate model performance against human labels, implement production guardrails, and ensure your LLM applications remain reliable and hallucination-free.

Key Features

01Comprehensive RAG evaluation metrics for retrieval accuracy and faithfulness

02Code-first and LLM-as-a-judge evaluator templates for Python and TypeScript

03Systematic error analysis and axial coding workflows to identify failure modes

04Validation workflows to ensure automated evaluators align with human judgment

05Experiment management tools for running batch evaluations and comparing datasets

068,664 GitHub stars

Use Cases

01Optimizing RAG systems by measuring retrieval relevance and groundedness

02Generating synthetic datasets to stress-test AI models before production deployment

03Setting up CI/CD guardrails to prevent regressions in LLM application performance

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add arize-ai/phoenix phoenix-evals

For use in Claude.ai and ChatGPT

Download Skill