Basic to Advanced: Retreival-Augmented Generation (RAG)

About this course

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 OllamaModel 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 FrameworkTex

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