deepeval-best-practices

What problem does it solve?

This Skill provides a concise, actionable guide for implementing and refining DeepEval-based evaluation workflows using the DeepEval and DeepTeam ecosystems. It covers RAG evaluation, AI agent testing, custom metrics (GEval/DAGMetric/BaseMetric), synthetic data generation, red-teaming, benchmarks, prompt optimization, and CI/CD integration with pytest.

Core Features & Use Cases

• Strategic guidance for end-to-end evaluation pipelines (end-to-end and component-level tracing)
• Best-practice patterns for RAG, agent, safety, and MCP evaluations
• Metrics design guidelines (GEval, DAGMetric, BaseMetric) and CI/CD regression testing with pytest
• Practical approaches to data synthesis, red-teaming, benchmarking, and prompt optimization
• Integration templates for CI/CD pipelines and observability dashboards

Quick Start

Follow this guide to establish a DeepEval-based evaluation workflow and run your first pytest-based regression test.