How to Systematically Setup LLM Evals (Metrics, Unit Tests, LLM-as-a-Judge)
Want to learn real AI Engineering? Go here: https://go.datalumina.com/iIO93Ps Want to start freelancing? Let me help: https://go.datalumina.com/vCTpbki 💼 Need help with a project? Work with me: https://go.datalumina.com/TMGbUvO 🔗 Download the free resources https://go.datalumina.com/QFs1X6H 🛠️ My VS Code / Cursor Setup https://youtu.be/mpk4Q5feWaw ⏱️ 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!
