Retrieval-augmented generation (RAG) is a robust paradigm that makes the most of the best information retrieval and generative model strengths to yield correct and context-relevant results. RAG enhances generative models by integrating external knowledge sources, making them more efficient in various use cases.

This course introduces the learners to the basic concepts of RAG, giving them a comprehensive understanding of RAG architecture and applications. You’ll implement RAG using LangChain, gaining practical experience building RAG pipelines. You’ll also create a complete frontend application for your RAG pipeline using Streamlit, providing a simple user interface for your project.

By the end of this course, you’ll be able to apply RAG principles and techniques to implement RAG solutions using LangChain and Streamlit.