🎥 Session 4 – RAG and Evaluation with LangChain & LangSmith | AI Engineer Training

Description

Welcome to Session 4 of the AI Bootcamp 2025! In this session, we dive deep into evaluating and optimizing RAG (Retrieval-Augmented Generation) pipelines using LangChain and LangSmith — the industry’s most powerful frameworks for building, testing, and monitoring LLM applications. Learn how to track performance, measure retrieval accuracy, and fine-tune your RAG systems for real-world impact. We’ll walk through hands-on examples, evaluation metrics, and best practices to make your AI models more accurate, reliable, and production-ready. By the end of this session, you’ll know how to use LangChain for RAG orchestration and LangSmith for deep evaluation — a must-have skill for modern AI engineers in 2025. 🚀 Topics Covered RAG evaluation fundamentals Integrating LangChain with vector stores & retrievers Using LangSmith for performance tracking and debugging Evaluating prompt quality, retrieval precision, and output relevance Real-world case studies for enterprise-grade AI apps 🧩 Problems Solved ✅ How to evaluate RAG performance effectively ✅ Monitoring and debugging LLM workflows ✅ Using LangChain & LangSmith together in production ✅ Making AI pipelines reliable, scalable, and explainable Session Highlights Live coding demos with LangChain Step-by-step RAG evaluation examples Real-world insights for AI engineers Focused on metrics, monitoring, and improvement #langchain #llm #aiengineering #aitraining #AIBootcamp2025 #machinelearning #retrievalaugmentedgeneration #aideployment #generativeai #vectorsearch #ai2025 #openlearnhub