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mlflow-evaluation

Evaluate Generative AI agents and LLM applications with MLflow scorers.

Updated Feb 27, 2026
Search Tags:#testing#monitoring#llm#agent#evaluation#mlflow#genai
One-click install
npx skills add https://github.com/LaurentPRAT-DB/LPT_claude_config --skill mlflow-evaluation-laurentprat-db
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mlflow-evaluation
Source: https://github.com/LaurentPRAT-DB/LPT_claude_config/tree/main/skills/mlflow-evaluation
Command: npx skills add https://github.com/LaurentPRAT-DB/LPT_claude_config --skill mlflow-evaluation-laurentprat-db

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you rigorously evaluate the quality, safety, and performance of your Generative AI agents and LLM applications, ensuring they meet your project's standards before deployment.

Core Features & Use Cases

  • Automated Evaluation: Run evaluations using built-in or custom scorers.
  • Trace Analysis: Debug agent behavior by analyzing execution traces.
  • Production Monitoring: Set up continuous quality checks on live traffic.
  • Use Case: You've built a customer support chatbot. Use this Skill to automatically evaluate its responses for safety, accuracy against expected answers, and adherence to brand guidelines, then monitor its performance in production.

Quick Start

Use the mlflow-evaluation skill to evaluate your agent using the provided test dataset and safety and guidelines scorers.

Frequently Asked Questions about mlflow-evaluation

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

FAQPage Schema
How do I evaluate Generative AI agents using MLflow?▼

Yes, you can debug agent behavior by analyzing MLflow execution traces to identify performance bottlenecks and errors. This tracing capability provides detailed insights for thorough debugging and quality assessment of your LLM applications.

Can I set up continuous monitoring for LLM applications in production?▼

Yes, you can set up continuous production monitoring via Unity Catalog to run quality checks on live traffic. This ensures your deployed Generative AI agents maintain safety, accuracy, and adherence to guidelines over time.

Does MLflow support custom scorers for LLM evaluation?▼

Yes, MLflow supports evaluating LLM applications with both built-in and custom scorers. You can automatically assess agent responses for safety, accuracy against expected answers, and adherence to specific brand guidelines.

What is the best way to assess a customer support chatbot for safety and accuracy?▼

The best way to assess a customer support chatbot is to use MLflow evaluation tools to automatically score responses for safety, accuracy against expected answers, and brand guidelines, then monitor its performance in production via Unity Catalog.

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