Feb 25
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Chatting with AI like ChatGPT feels simple: you type a prompt → you get a reply. But building a real AI application is a completely different challenge. Suddenly you’re dealing with: multiple prompts, chat history, memory, embeddings, vector databases, tool/API calls, multi-step reasoning, and structured outputs — and if you try to stitch all of that together manually, your code becomes a fragile mess. That’s exactly why LangChain exists. In this intro episode of our LangChain series on the NIIT YouTube channel, you’ll learn: ✅ What LangChain actually is (in simple terms) ✅ Why developers use it to build production-ready LLM apps ✅ The core building blocks: Prompts, Models, Parsers, Chains, Memory, Retrieval (RAG) ✅ How LCEL (LangChain Expression Language) makes workflows clean and composable ✅ Where LangChain fits in the modern AI stack: chatbots, PDF Q&A tools, RAG pipelines, tool-calling agents, multi-agent workflows. By the end of this video, you’ll know exactly where LangChain fits — and you’ll be ready for the next episodes where we build real projects step-by-step, fully hands-on. ⏱️ TIMESTAMPS / CHAPTERS 00:00 Series kickoff: what we’re building 00:20 Why LangChain matters 00:51 ChatGPT vs real AI apps 01:01 The moving parts (prompts, memory, embeddings, tools) 02:23 LangChain as building blocks 02:38 LCEL makes pipelines clean 02:57 Core components overview 03:01 Prompt templates 03:07 Models (switch LLMs easily) 03:16 Output parsers (JSON / structure) 03:25 Chains (pipeline steps) 03:34 Memory (multi-turn chat) 03:42 Retrieval (RAG + embeddings) 03:58 Real-world use cases 04:29 How LangChain changes how you build Want to learn by building real systems with tools, memory, retrieval, and evaluation — fully hands-on? Start here: 👉 https://www.niit.com/india/course/building-agentic-ai-systems/?utm_source=yt&utm_medium=video&utm_campaign=langchain_builder_series_intro_25feb26&utm_content=description_link https://www.niit.com/india/course/building-agentic-ai-systems/?utm_source=yt&utm_medium=video&utm_campaign=langchain_builder_series_intro_25feb26&utm_content=description_link 👇 Comment below: What should we build first — PDF Chat (RAG), a Local AI Dev Helper, a Resume–JD matcher, or an AI Agent with tool calling? #NIIT #UnlockWithNIIT #LangChain #LangChainTutorial #LCEL #RAG #AIAgents #LLM #GenAI #AIDevelopment #Python #Ollama #Llama3 #Streamlit #BuildWithAI