This playlist is your structured, no-nonsense guide to LLM Evaluation — the systematic process of measuring, debugging, and improving the quality of AI systems built with Large Language Models. Instead of relying on vibes, manual testing, or a handful of example prompts, this series teaches you how to evaluate LLM applications with the rigor needed to build reliable, production-ready systems.

Starting from the fundamentals of why LLM evaluation is fundamentally different from traditional software testing, the playlist walks through the complete evaluation lifecycle. You’ll learn how to think about evaluation as a continuous engineering process — defining what “good” looks like, creating meaningful evaluation datasets, designing evaluation criteria, and choosing the right methods to measure the performance of an LLM application.

From there, the series goes deeper into the practical side of evaluation: you’ll explore different evaluation strategies, understand the role of LLM-as-a-Judge, work with both traditional and LLM-based evaluation metrics, and learn how to assess dimensions such as correctness, relevance, groundedness, and response quality. You’ll also understand where automated evaluators work well, where they can fail, and why human evaluation still matters.

The playlist then moves towards building robust evaluation pipelines. You’ll learn how to structure experiments, compare different versions of prompts or models, analyze evaluation results, and use those insights to systematically improve your AI applications. The focus stays on practical implementation rather than treating evaluation as a purely theoretical exercise.

Throughout the series, concepts are explained with practical examples so you can see how evaluation works in real LLM applications — from defining evaluation criteria to running experiments and interpreting results.

By the end, you’ll stop thinking of LLM evaluation as simply “checking whether the answer looks good” and start treating it as an engineering discipline: something you can measure, automate, iterate on, and use to build AI systems you can actually trust.

Prerequisites: Basic Python, understanding of LLMs/Generative AI, and familiarity with building LLM applications

Watch on YouTube: https://www.youtube.com/playlist?list=PLEneLIDJFpcA

Standalone Price: ₹399