LLM Evaluation

Currently Playing: How to Evaluate LLM Applications: The Complete Workflow | CampusX

This lecture introduces the end-to-end LLM Application Evaluation Workflow. You’ll learn how AI engineers evaluate, improve, deploy, and monitor LLM-based systems using golden datasets, evaluation metrics, automated testing, and continuous feedback loops. Topics covered: * Defining tasks and success criteria * Creating golden datasets * Automated vs Human vs LLM-based evaluation * Running evaluations and measuring accuracy * Error analysis and system improvement * Production monitoring and feedback loops * Multiple evaluations for a single LLM application This lecture was taken live for insiders, join here: https://youtu.be/j_G30FLmCcw 📱 Grow with us: CampusX' LinkedIn: https://www.linkedin.com/company/campusx-official DM for Quick Convo: https://www.instagram.com/campusx.official My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789 Discord: https://discord.gg/PsWu8R87Z8 (@suf for queries) E-mail us at support@campusx.in Chapters 00:00 - Recap: Why, What and Types of LLM Evals 01:14 - Introduction to the LLM Application Evaluation Workflow 02:04 - Building an Email Classification LLM Application 04:01 - Step 1: Define the Task and Target 04:29 - Step 2: Define Success Criteria and Metrics 05:50 - Step 3: Build a Golden Evaluation Dataset 07:20 - Step 4: Choose an Evaluation Method 09:16 - Step 5: Run the Model on the Evaluation Dataset 10:32 - Step 6: Evaluate and Analyze Results 11:44 - Step 7: Improve the System (Prompt & Model Optimization) 12:36 - Step 8: Iterative Evaluation and Improvement Loop 13:39 - Step 9: Deployment and Production Monitoring 14:49 - Handling Production Failures and Dataset Expansion 15:58 - Why One LLM Application Needs Multiple Evaluations