How to Systematically Setup LLM Evals (Metrics, Unit Tests, LLM-as-a-Judge)

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⏱️ Timestamps
0:00 Introduction to Agentic AI Applications
1:54 Understanding LLM Evaluations
4:54 Core Challenges in LLM Development
7:54 Importance of Iteration and Improvement
9:21 Defining Evaluations in AI Systems
11:04 The Analyze, Measure, Improve Cycle
12:26 Levels of Evaluations
14:01 Unit Tests for LLMs
17:53 Human and Model Evaluations
22:44 Aligning LLM Evaluators
29:02 Process for Building Automated Evaluators
31:21 A/B Testing in AI Applications
34:40 Evaluation Metrics Overview
37:25 Common Mistakes to Avoid
39:46 Key Principles for Success
42:24 Conclusion and Next Steps

📌 Description
In this video, I go over the complete evaluation framework we use at Datalumina to systematically improve AI applications, taking you from basic unit tests all the way through human-aligned model evaluations and A/B testing. I share the exact process that separates the top 5% of AI engineers from those whose projects fail, including tools and code examples you can implement immediately to avoid becoming part of the 95% failure rate.

👋🏻 About Me
Hi! I’m Dave, AI Engineer and founder of Datalumina®. On this channel, I share practical tutorials that teach developers how to build production-ready AI systems that actually work in the real world. Beyond these tutorials, I also help people start successful freelancing careers. Check out the links above to learn more!

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