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$5+

RAG Framework course for Beginners (Generative AI)

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RAG Framework course for Beginners (Generative AI)

$5+

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.
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All code files (ipynb notebooks), a theory pdf and link to Video tutorials (free)

Codes
10 notebooks
Theory
PDF file
Video Tutorials
Yes, YT playlist
Size
475 KB
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