Basic to Advanced: Retreival-Augmented Generation (RAG)
Multi-modal RAG Stack: A Hands-on Journey Through Vector Stores, LLM Integration, and Advanced Retrieval Methods
Basic to Advanced: Retreival-Augmented Generation (RAG) free download
Multi-modal RAG Stack: A Hands-on Journey Through Vector Stores, LLM Integration, and Advanced Retrieval Methods
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
Website Chatbot
Architecture and implementation
Content indexing and retrieval
Response generation and optimization
SQL Chatbot
Natural language to SQL conversion
Query optimization and safety
Database integration best practices
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.
