- Course
Building RAG Applications with LangChain
Retrieval-Augmented Generation (RAG) is a key pattern for building AI applications that generate accurate, grounded responses. This course will teach you how to develop end-to-end, production-ready RAG pipelines using LangChain.
- Course
Building RAG Applications with LangChain
Retrieval-Augmented Generation (RAG) is a key pattern for building AI applications that generate accurate, grounded responses. This course will teach you how to develop end-to-end, production-ready RAG pipelines using LangChain.
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This course is included in the libraries shown below:
- AI
What you'll learn
Large language models are powerful, but with limitations. They are only as effective as the information they can access. In this course, Building RAG Applications with LangChain, you’ll gain the ability to design, build, and optimize production-ready RAG pipelines using LangChain. First, you’ll explore the core concepts of RAG architecture, document processing, embeddings, and retrieval. Next, you’ll discover advanced retrieval techniques, including query transformation, multi-step retrieval, self-querying retrievers, and metadata filtering to improve retrieval quality. Finally, you’ll learn how to optimize the complete RAG pipeline by managing context effectively, engineering prompts, and building reliable answer generation workflows. When you’re finished with this course, you’ll have the skills and knowledge needed to to build scalable, accurate, and production-ready RAG applications with LangChain.