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Welcome to the LangChain Course! I’m Janakiram MSV, your instructor for this journey into building modern AI applications. In this lesson, you’ll learn how to leverage LangChain’s orchestration framework to integrate large language models (LLMs) with databases, APIs, and the web, creating powerful generative AI solutions.

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What You’ll Learn

  • Core building blocks of LLM-based applications
  • Shared architecture of ChatGPT, Google Gemini, and Microsoft Copilot
  • Deep dive into LangChain’s modules: model I/O, the LangChain Expression Language (LCEL), chains, memory, tools, and agents

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Course Structure

ComponentDescription
LectureTheory and architectural patterns
Hands-On DemoLive code walkthroughs in KodeKloud labs
Self-PracticeInteractive notebooks—bring your own OpenAI API key
You’ll need your own OpenAI API key (and any other third-party credentials) to complete the KodeKloud labs.

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LangChain Module Overview

ModuleUse Case
Model I/OQuery and manage LLMs for text generation and analysis
LangChain Expression Language (LCEL)Create reusable prompt templates and transformations
ChainsOrchestrate multi-step LLM workflows
MemoryMaintain state and context across conversations
ToolsConnect to REST APIs, databases, and external services
AgentsAutomate decision logic with LLM-driven agents

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Next Steps

By the end of this course, you’ll be proficient with LangChain’s core features and ready to build end-to-end generative AI applications. Join the KodeKloud Community Forum to ask questions, share your projects, and collaborate with fellow learners.

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