Apr 11
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This complete LangChain Masterclass is a carefully curated compilation of our LangChain video series, taking you from absolute basics to advanced, production-ready AI systems — all in one place. ⚠️ LangGraph videos are intentionally excluded to keep the focus purely on LangChain. Whether you’re a beginner, AI enthusiast, student, or AI engineer, this long-form video will help you understand how modern LLM applications actually work under the hood and how to build them step by step using LangChain. 🔍 What You’ll Learn in This Video 🧠 LangChain Foundations What LangChain is and why it’s used How LangChain compares to raw LLM API calls Tokens, context windows & chat models Prompt engineering fundamentals PromptTemplate & ChatPromptTemplate (dynamic prompts) 🛠 Structured & Reliable LLM Outputs JSON outputs & schema enforcement TypedDict & Pydantic structured outputs Output Parsers (String, JSON, Pydantic) Validating and parsing LLM responses 🔗 Chains & Workflow Design Simple, Sequential, Conditional & Parallel chains Designing clean, modular AI pipelines Best practices for scalable LLM workflows 📚 Retrieval-Augmented Generation (RAG) Document Loaders (PDF, Web, CSV, Directories) Text splitters & chunking strategies Embeddings explained (vector representations) VectorStores (FAISS vs Chroma) Retrievers (VectorStore, Wikipedia, MMR) End-to-end RAG system implementation Explainable RAG with source citations Conversational RAG with memory Multi-document RAG with cross-document reasoning 🤖 Agents & Advanced Systems Why RAG alone is not enough LangChain agents explained Building your first AI agent from scratch Tools & agent reasoning LLM-as-a-Judge and self-evaluating AI systems Agentic loops & autonomous improvement 🎯 Who This Video Is For Beginners learning LangChain step by step AI Engineers building real-world LLM apps Developers working on RAG, chatbots, or AI assistants Anyone preparing for agentic AI systems 🧩 Tech Stack Used LangChain (latest APIs) Python Gemini / LLaMA / HuggingFace models FAISS & Chroma Vector Databases Streamlit (for UI examples) 📌 By the end of this video, you’ll have a crystal-clear mental model of LangChain and the confidence to build real, scalable, production-ready LLM applications. 👍 If you find this helpful, like the video and subscribe for more deep-dive AI engineering content.