AI Evaluations: Foundations and Practical Examples
2h 7mBeginner2025-09-22
Authors
Mahesh Yadav
Course details
AI agents are helping us achieve more and spend less. Although it's easier than ever to build AI agents, it can be challenging to evaluate their performance. In this course, generative AI advisor Mahesh Yadav shares techniques that allow you to go from zero to hero in evaluating AI agents. Learn how to set up your AI agent evaluation and scale it. Explore tricks and tips that will save you time and money as you build an evaluation strategy for your AI agents and execute that strategy. When you complete this course, you'll have a comprehensive evaluation plan for testing AI agents.
Learning objectives
Define requirements to evaluate and choose the right foundational models for your AI agents.
Create a robust human evaluation plan that enables you to build and ship AI agents into production.
Scale your evaluations using AI—whether with out-of-the-box tools or with custom LLMs acting as judges.
Understand, identify, and execute on a scalable AI agent evaluation plan.
Learning objectives
Define requirements to evaluate and choose the right foundational models for your AI agents.
Create a robust human evaluation plan that enables you to build and ship AI agents into production.
Scale your evaluations using AI—whether with out-of-the-box tools or with custom LLMs acting as judges.
Understand, identify, and execute on a scalable AI agent evaluation plan.
Skills covered
AI Productivity ToolsArtificial Intelligence FoundationsArtificial Intelligence for BusinessArtificial Intelligence (AI)Business Software and ToolsOne-Off
Concepts
Introduction
- The power of AI agents and AI evaluations
Introducing AI Agents and Evaluations
- Demo of fully functional human and auto-evaluator systems
- What are AI agents
- Why a lot of AI agents fail
- Understanding the moat in AI agents
- Evaluating the moat and backbone of your AI agents
- Challenges in setting proprietary AI evaluations
Foundation Models and Benchmarks in AI
- Introduction to AI foundation models
- Essential requirements for model evaluations
- Define requirements for model evaluations
- Understanding and leveraging benchmarks
- Hands-on lab - Choosing the right model with benchmark analysis
Manual Evaluation Strategies and AI Component-Level Testing
- Decomposing AI agents into evaluative components
- Identifying high-risk or hard-to-evaluate components
- Manual evaluation with criteria
- Defining evaluation criteria from MVP to GA
- Hands-on lab - Vibe code auto evaluations using Cursor
- Hands-on lab - Automating AI evaluation using LLM as judge
Automated Evaluation Techniques and Metrics Deep Dive
- Deep dive into evaluation metrics for AI agents
- Hands-on lab - Building an automated evaluator
- Red teaming - Scaling automated evaluations without ground truth
- Continuous evaluation with real-time monitoring and alerts
Conclusion
- What's next