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Developing Applications with LangChain

Uppdaterad 2026-03
Learn how to build impactful LLM applications, including RAG workflows and agentic systems, using the LangChain framework!
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PythonArtificial Intelligence9 timmar6,856

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Developing Applications with LangChain

Building applications involving LLMs can be challenging due to the sheer number of components involved: prompts, models, vector databases, APIs, and agents. Enter LangChain! LangChain is a framework for building and orchestrating components using a single, unified syntax.In this skill track, you'll use LangChain to master building the most common LLM applications in Python. You'll get to grips with the fundamentals of building impactful chatbots using models from Hugging Face and OpenAI.You'll discover Retrieval Augmented Generation, or RAG, which allows you to integrate your own data into your application, so the model can go beyond its training data.Agentic systems are one of the most exciting developments in AI and LLM application development, and you'll get a full crash course on building your own! Agents use LLMs to make decisions, in effect, deciding to take different actions based on the input. These actions could involve calling APIs, running Python code, or even performing RAG!Along the way, you'll apply your new-found knowledge in hands-on, interactive projects. Join the Generative AI generation today!

Förkunskapskrav

Det finns inga förkunskapskrav för detta spår

  • Course

    1

    Developing LLM Applications with LangChain

    Discover how to build AI-powered applications using LLMs, prompts, chains, and agents in LangChain.

  • Project

    Bonus

    Building RAG Chatbots for Technical Documentation

    Implement retrieval augmented generation (RAG) with LangChain to create a chatbot for answering questions about technical documentation.

Developing Applications with LangChain
3 courses
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