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mlflow-genai-evaluation

Automate Generative AI agent evaluation with MLflow GenAI and custom scorers.

1|Updated Jan 22, 2026
Search Tags:#testing#monitoring#llm#agent#evaluation#mlflow#genai
One-click install
npx skills add https://github.com/prashsub/vibe_coding_lakehouse_starter_repo --skill mlflow-genai-evaluation-prashsub
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mlflow-genai-evaluation
Source: https://github.com/prashsub/vibe_coding_lakehouse_starter_repo/tree/main/data_product_accelerator/skills/genai-agents/02-mlflow-genai-evaluation
Command: npx skills add https://github.com/prashsub/vibe_coding_lakehouse_starter_repo --skill mlflow-genai-evaluation-prashsub

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill streamlines the evaluation of Generative AI agents by providing robust patterns for using MLflow's GenAI evaluation capabilities, including LLM-as-judge scorers and custom metrics. It addresses the complexity of setting up reliable, production-grade evaluation pipelines.

Core Features & Use Cases

  • LLM-as-Judge Evaluation: Implement sophisticated evaluations using LLMs to score agent responses.
  • Custom Scorers: Develop and integrate domain-specific evaluation metrics.
  • Prompt Optimization: Utilize GEPA for automated prompt tuning.
  • Judge Alignment: Align LLM judges with domain feedback using MemAlign.
  • Production Monitoring: Set up Unity Catalog trace ingestion for ongoing performance tracking.
  • Use Case: When deploying a new AI agent, use this Skill to run a comprehensive evaluation suite against a benchmark dataset, ensuring it meets predefined quality and safety thresholds before going live.

Quick Start

Use the mlflow-genai-evaluation skill to set up and run an agent evaluation pipeline with custom scorers and threshold checks.

Frequently Asked Questions about mlflow-genai-evaluation

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

FAQPage Schema
How do I evaluate GenAI agents using MLflow?▼

To evaluate GenAI agents using MLflow, this Skill automates the process by leveraging the mlflow.genai.evaluate() function to run comprehensive evaluation pipelines against benchmark datasets with custom scorers.

How do I implement an LLM-as-judge scorer for agent evaluation?▼

Implementing an LLM-as-judge scorer involves using LLMs to score agent responses, with judge alignment supported by MemAlign to align the LLM judges with specific domain feedback data.

Can I optimize LLM prompts automatically during the evaluation process?▼

You can optimize LLM prompts automatically during evaluation by utilizing GEPA for automated prompt tuning, ensuring the GenAI agents meet predefined quality and safety thresholds.

How do I monitor GenAI agents in production with MLflow?▼

Monitoring GenAI agents in production with MLflow requires setting up Unity Catalog trace ingestion to track ongoing performance and ensure continuous quality evaluation.

Does MLflow GenAI evaluation support custom domain-specific metrics?▼

MLflow GenAI evaluation supports custom scorers for domain-specific metrics, utilizing a helper function to extract response text and the Databricks SDK to execute the LLM calls.

What is the best way to check quality thresholds before deploying an AI agent?▼

The best way to check thresholds before deployment is to run a comprehensive evaluation suite using mlflow.genai.evaluate(), applying metric aliases and threshold checking to verify quality.

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