📚 AI Agent Evaluation Series - Part 5 of 5

1. Observability & Evals: Why They Matter ←
2. Human-in-the-Loop Evaluation ←
3. Implementing Automated Evals ←
4. Debugging AI Agents ←
5. Human Review Training Guide ← You are here

Human Review Training: Scoring AI Agents in Langfuse for Better Evaluation

If you're a domain expert who's been asked to review AI agent responses in Langfuse, this guide is for you. You don't need to be a technical expert or an AI specialist—you just need to bring your domain knowledge and judgment to help improve the AI system.

This is training content designed to help you understand exactly what to look for, how to score responses, and how to provide feedback that makes a real difference. Think of this as your handbook for becoming an effective AI reviewer.

What you'll learn:

• The exact 1-5 scoring rubric with real examples
• How to use Langfuse's annotation queue efficiently
• Copy-paste comment templates for common scenarios
• Best practices for consistent, high-quality reviews
• Why your 1-2 minutes of judgment matters more than spending hours

Let's get you ready to make a meaningful impact.

📚 AI Agent Evaluation Series - Part 2 of 5

1. Observability & Evals: Why They Matter ←
2. Human-in-the-Loop Evaluation ← You are here
3. Implementing Automated Evals →
4. Debugging AI Agents →
5. Human Review Training Guide →

Human at the Center: Building Reliable AI Agents with Your Feedback

You're not training your replacement—you're scaling your judgment.

Human-in-the-loop (HITL) means experts stay in the driver's seat. The agent proposes; you decide what "good" looks like. Over time, your feedback turns sporadic wins into consistent performance.