Deep dive into Retrieval-Augmented Generation (RAG) framework in this concise, high-impact course designed for AI professionals and enthusiasts. Learn how RAG integrates Large Language Models (LLMs) to answer questions based on your external files like PDFs, Text files, CSVs, YouTube videos, etc. Perfect for developers, data scientists, and AI practitioners looking to stay ahead.

Note: This course uses Free LLM APIs, so don't worry for any extra spending !

Theory

• Introduction to Generative AI & Machine Learning
  Explore types of machine learning models, what generative AI is, and the role of LLMs.
• What is RAG?
  A clear breakdown of the RAG framework—how it works, its components, and why it’s becoming a game-changer and its important hyperparameters.
• Vector Databases & Their Role in RAG
  Compare Vector DBs with traditional RDBMS. Learn about core elements
• GraphRAG (Overview)
  Discover the advantages of GraphRAG, how it works.
• HybridRAG (Overview)
  Delve into HybridRAG and how it blends GraphRAG and StandardRAG to produce the best results.

Codes

• Your First RAG App
  Create a basic RAG application using a simple text file.
• Vector DB Setup
  Learn to set up a Vector Database independently of RAG, a crucial step in efficient data retrieval.
• RAG Hyperparameters
  Discover the key hyperparameters that significantly impact RAG performance.
• Using Different File Formats
  Implement RAG across various formats like videos, CSV files, and more for broader applicability.
• RAG + Internet Integration
  Enable Internet search in RAG using SerpAPI to enhance real-time retrieval capabilities.
• Multi-Document RAG
  Build RAG systems that communicate with multiple documents simultaneously for better context and accuracy.
• Handling Hallucinations
  Address AI hallucinations (inaccurate responses) by prompting for references, ensuring more reliable outputs.
• Recommendation System using LLMs
  Build a Recommendation System using RAG, blending information retrieval with AI recommendations.
• GraphRAG with LangChain
  Implement GraphRAG using LangChain, showcasing the power of combining graph structures with RAG.
• Hybrid RAG
  Explore the advanced technique of combining RAG and GraphRAG for hybrid, high-performance retrieval.