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HyunjunJeon
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deepeval-best-practices

Document DeepEval and DeepTeam best practices for LLM evaluation workflows.

Updated Aug 27, 2026
Search Tags:#pytest#llm#evaluation#best-practices#ci-cd#deepeval#deepteam
One-click install
npx skills add https://github.com/HyunjunJeon/SDS-AX-Advanced-2026-1 --skill deepeval-best-practices
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: deepeval-best-practices
Source: https://github.com/HyunjunJeon/SDS-AX-Advanced-2026-1/tree/main/Day-05/.agents/skills/deepeval-best-practices
Command: npx skills add https://github.com/HyunjunJeon/SDS-AX-Advanced-2026-1 --skill deepeval-best-practices

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

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.

Frequently Asked Questions about deepeval-best-practices

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I evaluate RAG applications using pytest and DeepEval?▼

DeepEval evaluates RAG applications by integrating with pytest to run regression tests on retrieval and generation components. This Skill provides best-practice patterns for end-to-end RAG evaluation, ensuring safe and scalable testing workflows.

What is the best way to create custom metrics for LLM evaluation?▼

Create custom LLM evaluation metrics using DeepEval's GEval, DAGMetric, or BaseMetric classes. This Skill provides design guidelines for building and applying custom metrics to evaluate model outputs accurately.

How do I integrate LLM evaluation into a CI/CD pipeline?▼

Integrate LLM evaluation into CI/CD pipelines by running DeepEval tests with pytest. This Skill offers integration templates and best practices for automating regression testing within continuous deployment workflows.

Can I use DeepEval for red-teaming and synthetic data generation?▼

Yes, DeepEval and DeepTeam support red-teaming and synthetic data generation. This Skill documents practical approaches to generate test data and identify vulnerabilities in AI agents.

Does DeepEval support AI agent testing and prompt optimization?▼

Yes, DeepEval supports AI agent testing and prompt optimization. This Skill provides best-practice patterns for agent evaluations, safety testing, and refining prompts using structured benchmarks.

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