Basic to Advanced: Retreival-Augmented Generation (RAG)

Descripción

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.

Lo que aprenderás

• 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
• Understand how to deploy on AWS EC2 (Basic Guide)

Requisitos

• Basic Python knowledge is Good to have but not mandatory.