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- AI
LangChain
LangChain is a powerful framework for building production-ready applications powered by Large Language Models (LLMs). It provides the tools and abstractions needed to create sophisticated AI systems including retrieval-augmented generation (RAG), conversational AI, and agentic applications. This path teaches you to build, evaluate, deploy, and monitor LangChain applications using industry-standard practices and tools.
By completing this path, you'll gain comprehensive expertise in building LLM applications from foundational concepts through production deployment. You'll learn to implement RAG systems for knowledge retrieval, create conversational AI with proper memory management, build tool-using applications with agentic patterns, and deploy monitored, production-ready systems using LangSmith and LangServe.
This learning path is actively in production. More content will be added to this page as it gets published and becomes available in the library. Planned content includes: - Introduction to LangChain - Working with Data: Retrieval and Vector Stores in LangChain - Building RAG Applications with LangChain - Memory, State, and Conversational Applications in LangChain - Agents, Tools, and Planning Foundations with LangChain - Production Deployment and Advanced Topics in LangChain - Lab: Build and Evaluate a Document Q&A RAG Application with LangChain - Lab: Design and Deploy a Research Assistant with LangChain
Content in this path
LangChain
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What You'll Learn
- How to build production-ready LLM applications using LangChain's core components including prompt templates, chains, retrievers, and the LangChain Expression Language (LCEL)
- How to design and implement Retrieval-Augmented Generation (RAG) systems, conversational AI with memory management, and tool-using applications with agentic patterns
- How to evaluate, optimize, and deploy LangChain applications to production using LangServe and LangSmith with proper monitoring, cost management, and security practices
- Learners should have intermediate Python programming proficiency, including comfort with functions, classes, and working with third-party libraries. Familiarity with REST APIs and basic web service concepts is essential. A general understanding of what Large Language Models (LLMs) are and their capabilities is helpful, but no machine learning or data science expertise is required.
- LangChain
- Generative AI
- Large Language Models
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