LLM Evaluation

Currently Playing: Why Your AI Application Needs Multiple Eval Pipelines? | CampusX

In this lecture of the LLM Evaluation Masterclass, we break down why a robust AI system demands multiple, distinct evaluation pipelines operating in parallel. Notes: https://1drv.ms/o/c/85452F67DAA1111C/IgCIRXGQVQm8R7-eF2h5lZ4NAayeX_9R3V4XHDDmsXYMnrk 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 CampusX on Instagram for daily tips: https://www.instagram.com/campusx.official My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789 Discord: https://discord.gg/PsWu8R87Z8 E-mail us at support@campusx.in Chapters: 00:00 - Recap: Why We Need Evals & Model vs. Application Testing 02:22 - The Last Session’s Core Statement: Multiple Eval Pipelines 03:32 - Dissecting a Retrieval-Augmented Generation (RAG) Setup 04:33 - Understanding Application Failure Points 05:45 - Independent Success vs. Combined System Failure 09:48 - Walkthrough Scenario: The "8-Week ML Course Duration" Trap 14:00 - Structuralizing Checks: Moving from Component to Workflow Level 17:00 - Why Pipeline Safety Demands Application-Level Performance Audits 17:46 -Summary of the 3 Eval Levels: Component, Workflow, and Application 19:26 - Introduction to Multi-Dimensional Risk Categories 21:54 - The 3 Pillars: Application Quality, Safety, and Operations 23:14 - Deep Dive: Specific Metrics for Summary, RAG, Chatbots, and Agents 26:12 - The Safety Guardrails Matrix: Toxicity, Leaks, and Jailbreak Resistance 27:10 - Operational Audits: Calculating Latency Under Load and Cost Per Request 27:39 - Final Conclusion