Basic to Advanced: Retreival-Augmented Generation (RAG) (Udemy.com)

Transform your development skills with our comprehensive course on Retrieval-Augmented Generation (RAG) and LangChain. Whether you're a developer looking to break into AI or an experienced programmer wanting to master RAG, this course provides the perfect blend of theory and hands-on practice to help you build production-ready AI applications.

What You'll Learn

• Build three professional-grade chatbots: Website, SQL, and Multimedia PDF

• Master RAG architecture and implementation from fundamentals to advanced techniques

• Run and optimize both open-source and commercial LLMs

• Implement vector databases and embeddings for efficient information retrieval

• Create sophisticated AI applications using LangChain framework

• Deploy advanced techniques like prompt caching and query expansion

Course Content

Section 1: RAG Fundamentals

• Understanding Retrieval-Augmented Generation architecture

• Core components and workflow of RAG systems

• Best practices for RAG implementation

• Real-world applications and use cases

Section 2: Large Language Models (LLMs) - Hands-on Practice

• Setting up and running open-source LLMs with Ollama

• Model selection and optimization techniques

• Performance tuning and resource management

• Practical exercises with local LLM deployment

Section 3: Vector Databases & Embeddings

• Deep dive into embedding models and their applications

• Hands-on implementation of FAISS, ANNOY, and HNSW methods

• Speed vs. accuracy optimization strategies

• Integration with Pinecone managed database

• Practical vector visualization and analysis

Section 4: LangChain Framework

• Text chunking strategies and optimization

• LangChain architecture and components

• Advanced chain composition techniques

• Integration with vector stores and LLMs

• Hands-on exercises with real-world data

Section 5: Advanced RAG Techniques

• Query expansion and optimization

• Result re-ranking strategies

• Prompt caching implementation

• Performance optimization techniques

• Advanced indexing methods

Section 6: Building Production-Ready Chatbots

1. Website Chatbot

   • Architecture and implementation

   • Content indexing and retrieval

   • Response generation and optimization

2. SQL Chatbot

   • Natural language to SQL conversion

   • Query optimization and safety

   • Database integration best practices

3. Multimedia PDF Chatbot

   • Multi-modal content processing

   • PDF parsing and indexing

   • Rich media response generation

Who This Course is For

• Software developers looking to specialize in AI applications

• AI engineers wanting to master RAG implementation

• Backend developers interested in building intelligent chatbots

• Technical professionals seeking hands-on LLM experience

Prerequisites

• Basic Python programming knowledge

• Familiarity with REST APIs

• Understanding of basic database concepts

• Basic understanding of machine learning concepts (helpful but not required)

Why Take This Course

• Industry-relevant skills currently in high demand

• Hands-on experience with real-world examples

• Practical implementation using Tesla Motors database

• Complete coverage from fundamentals to advanced concepts

• Production-ready code and best practices

• Workshop-tested content with proven results

What You'll Build

By the end of this course, you'll have built three professional-grade chatbots and gained practical experience with:

• RAG system implementation

• Vector database integration

• LLM optimization

• Advanced retrieval techniques

• Production-ready AI applications

Join us on this exciting journey to master RAG and LangChain, and position yourself at the forefront of AI development.