Fundamentals of Retrieval-Augmented Generation with LangChain

Fundamentals of Retrieval-Augmented Generation with LangChain

This course covers RAG basics, architecture, and applications and teaches you to build RAG pipelines using LangChain and Streamlit.

Beginner

21 Lessons

4h

Certificate of Completion

This course covers RAG basics, architecture, and applications and teaches you to build RAG pipelines using LangChain and Streamlit.

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

27 Playgrounds

This course includes

27 Playgrounds

Course Overview

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 pract...Show More

TAKEAWAY SKILLS

Generative Ai

Large Language Models (llms)

What You'll Learn

An understanding of the basics of retrieval-augmented generation (RAG)

Hands-on experience implementing RAG using LangChain

The ability to create a frontend application for the RAG pipeline using Streamlit

Hands-on experience applying the learned skills to solve a real-world use case

What You'll Learn

An understanding of the basics of retrieval-augmented generation (RAG)

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Course Content

1.

Getting Started

In this chapter, you will discover what RAG is and why this course might be a good fit for you.
2.

The Basics of RAG

In this chapter, you will explore the essential components of RAG, including indexing techniques and retrieval strategies.
3.

RAGs and LangChain

In this chapter, you will learn how to implement RAG systems using LangChain, covering key topics such as document indexing and retrieval.
4.

Build a Frontend for Our RAG System

In this chapter, you will learn how to build a user-friendly frontend for your RAG system using Streamlit.
6.

Conclusion

1 Lesson

In this concluding chapter, you'll review the key concepts covered in the course and explore potential next steps for further learning.

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