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.
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This course includes
This course includes
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...
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
Getting Started
The Basics of RAG
RAGs and LangChain
Build a Frontend for Our RAG System
Challenges
Conclusion
1 Lesson
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