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Want to build agentic AI (company) chatbots fast? This hands-on lab breaks down LangChain’s core components and shows how to assemble a production-ready chatbot: prompt templates, memory, vector search (RAG), model plug-ins and lightweight pipelines using LangChain patterns. What you’ll learn (in this video) What LangChain actually is and why it’s the abstraction layer teams use to build agentic software. How LangChain gives vendor independence so you can swap LLMs with minimal code changes. The pre-built building blocks: prompts, embeddings, vector stores (e.g., Pinecone, Chroma), memory, agents and tools. Prompt engineering patterns: templates, chat templates (system/human/assistant) and few-shot techniques. LangChain Expression Language (LCEL) & simple pipeline composition (prompt → model → parser). Implementing memory + RAG: chunking docs, embedding, indexing into a vector DB and retrieving contextual answers. A short walkthrough to assemble a simple company chatbot you can extend to production. Who this is for Developers, ML engineers, product managers, and maker founders who want to go beyond single LLM calls and build autonomous, context-aware assistants connected to internal knowledge. Pro tips Start with a small set of docs to tune chunk size & embedding settings. Use conversational memory for UX, but persist critical state to a DB for production. Keep prompts modular (templates + variables) so you can A/B test quickly. If this helped, like & subscribe for more short, practical labs on LLM engineering. Drop questions below — what should I build next with LangChain? #LangChain #LangChainExplained #AIAgent #AgenticSoftware #LLMs #GPT #OpenAI #Anthropic #Gemini #AI #Chatbot #BuildAChatbot #RAG #RetrievalAugmentedGeneration #VectorDatabase #Chroma #Pinecone #FISS #SemanticSearch #LCEL #LangChainExpressionLanguage #Programming #Python #SoftwareDevelopment #AITools