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⚡ Welcome to another session in the LangGraph Workflow Series with Nidhi Chouhan! In this video, we explore the Evaluator-Optimizer Pattern — a key concept for improving and refining AI workflows. You’ll learn how to make your LangGraph pipelines smarter, adaptive, and capable of self-improvement through evaluation and optimization cycles. 💡 What You’ll Learn: ✅ What is the Evaluator-Optimizer pattern? ✅ How it enhances LLM performance in LangGraph ✅ Using evaluation loops to refine outputs ✅ Building self-correcting and feedback-driven AI workflows This workflow helps your AI system analyze its own performance and continuously optimize results — just like human feedback systems! 📘 Part of: LangGraph Workflow Series — building practical, Agentic AI workflows step by step. 📌 GitHub Repository (Code + Notes): 👉 https://github.com/dearnidhi/Agentic-AI-HandsOn-Bootcamp 📩 Connect with Me: ✉️ Email: nidhiyachouhan12@gmail.com 📸 Instagram: @codenidhi | @dear_nidhi 💼 LinkedIn: Nidhi Chouhan ✨ Don’t forget to LIKE 👍, SHARE 📢 & SUBSCRIBE 🔔 for more tutorials on LangGraph, LLMs, and Agentic AI. LangGraph Workflow, LangGraph Evaluator, LangGraph Optimizer, Evaluator Optimizer Pattern, LangGraph Feedback Loop, LangGraph Self-Improving AI, Agentic AI Optimization, LangGraph Evaluation Pipeline, LangGraph Tutorial, LangGraph Performance Tuning, LangGraph Auto Evaluation, LangChain vs LangGraph, Nidhi Chouhan LangGraph #LangGraph #AgenticAI #EvaluatorOptimizer #AIWorkflow #LangChain #CodeNidhi #NidhiChouhan #GenAI #LangGraphTutorial #ArtificialIntelligence #MachineLearning #LLM #Optimization