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UdemyLangChain in Action: Develop LLM-Powered Applications
4.5(1,437)7k students
UdemyFreshly updated for LangChain 1.0.x, this course covers the current generation of the framework, LCEL, LangGraph-based orchestration, the revamped Agents API, and the newer langchain_classic import structure, rather than teaching against an outdated version. It starts from basic OpenAI API usage and prompt templates before moving into Chains, Callbacks, and Memory for building context-aware systems.
Listed as intermediate level, and pitched at both newcomers to LangChain and practitioners who already have some AI development experience. Given the pace toward advanced topics, some comfort with general programming concepts will help.
The back half is where it earns its keep: Retrieval Augmented Generation, autonomous agents, hybrid search, the Indexing API, and LangSmith for observability, closing with microservice architecture patterns for LLM applications. For a framework that changes as fast as LangChain does, a course explicitly rebuilt around its 1.0.x release is worth more than an older one that's quietly gone stale.
This course provides an in-depth exploration into LangChain, a framework pivotal for developing generative AI applications.
Now fully updated for LangChain 1.0.x — including LCEL, LangGraph-based orchestration, the revamped Agents API, and the langchain_classic imports.
Aimed at both beginners and experienced practitioners in the AI world, the course starts with the fundamentals, such as the basic usage of the OpenAI API, progressively delving into the more intricate aspects of LangChain.
You'll learn about the intricacies of input and output mechanisms in LangChain and how to craft effective prompt templates for OpenAI models. The course takes you through the critical components of LangChain, such as Chains, Callbacks, and Memory, teaching you to create interactive and context-aware AI systems.
Midway, the focus shifts to advanced concepts like Retrieval Augmented Generation (RAG) and the creation of Autonomous Agents, enriching your understanding of intelligent system design. Topics like Hybrid Search, Indexing API, and LangSmith will be covered, highlighting their roles in enhancing the efficiency and functionality of AI applications.
Toward the end, the course integrates theory with practical skills, introducing Microservice Architecture in large language model (LLM) applications and the LangChain Expression Language. This ensures not only a theoretical understanding of the concepts but also their practical applications.
This course is tailored for individuals with a foundational knowledge of Python, aiming to build or enhance their expertise in AI. The structured curriculum ensures a comprehensive grasp of LangChain, from basic concepts to complex applications, preparing you for the future of generative AI.
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