LangChain 2026 Day 9: Evaluating RAG with LLM-as-a-Judge

Description

Welcome to Day 9 of my LangChain 2026 Course! Building an AI is easy. Knowing if it works is hard. Today, we build a robust Evaluation Framework for our agent. We implement the "LLM-as-a-Judge" pattern to automatically score our RAG responses against a Golden Dataset. We will write a scoring engine that rates accuracy, clarity, and faithfulness on a scale of 1-5. In this episode you’ll learn: How to benchmark RAG applications Creating a "Golden Dataset" (Ground Truth) Implementing the LLM-as-a-Judge pattern Calculating accuracy scores automatically Moving from "Vibe Checks" to Data-Driven Dev 📌 GitHub Code: https://github.com/sebuzdugan/langchain-2026 📚 Full Playlist: https://www.youtube.com/playlist?list=PLH2Jo7IpHaBR3uuBh8HUqjAPbwL575XNB