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.
주요 기능
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
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사용 사례
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
