Mastering Retrieval-Augmented Generation (RAG)

Master RAG from Zero to Hero: Build Real-World AI with Retrieval-Augmented Generation

What you'll learn
Core principles of Retrieval-Augmented Generation (RAG) – Understand how RAG combines retrieval and generation for improved AI responses.
Implementing basic and advanced RAG architectures – Step-by-step guides to setting up RAG, Multi-Query RAG, RAG Fusion, and HyDE RAG.
Working with OpenAI embeddings and Pinecone – Practical exercises in connecting embeddings with vector databases for efficient retrieval.
Multi-query and RAG Fusion techniques – Learn strategies for better, contextually accurate answers through fusion and multi-query models.
Building and deploying RAG with FastAPI on Google Cloud Platform (GCP) – End-to-end deployment guidance for scalable RAG applications.
Prompt routing and database management – Gain experience with routing strategies and optimized content indexing for more efficient RAG systems.
Prompt caching and optimization techniques – Discover ways to reduce costs and improve response speed with caching in RAG models.

Requirements
Basic knowledge of Python programming
Prompt Engineering: Writing basic to intermediate prompts
Understanding of machine learning fundamentals

Description
Welcome to "Mastering Retrieval-Augmented Generation (RAG): From Zero to Hero"!This course is your all-in-one guide to understanding and implementing Retrieval-Augmented Generation (RAG) — a game-changing approach to enhance AI responses with powerful retrieval capabilities. Through hands-on projects, real-world exercises, and step-by-step tutorials, you'll quickly learn how to leverage RAG architectures to build effective and scalable AI solutions.This course is designed for AI practitioners, data scientists, machine learning engineers, and developers with a background in Python programming and a basic understanding of machine learning and NLP concepts.What You'll Learn:- Core RAG Architecture – Understand how RAG works, from basic concepts to advanced multi-query, Fusion, and HyDE architectures.- OpenAI Embeddings and Pinecone Integration – Learn how to connect OpenAI embeddings with Pinecone for efficient content retrieval.- Building RAG Models from Scratch – Implement multi-query and Fusion RAG models with hands-on exercises.- Advanced RAG Techniques – Explore database and prompt routing, caching, and deployment for optimized RAG solutions.- Deploying on Google Cloud Platform (GCP) with FastAPI – Deploy your RAG models in a scalable cloud environment with detailed deployment instructions. Who This Course is For:This course is ideal for those with a background in software engineering, Python programming, and basic ML knowledge who are eager to dive into RAG applications. It’s packed with exercises to build your expertise from scratch, making it suitable for those new to RAG while being comprehensive enough for seasoned AI practitioners looking to expand their skills.Join us and become proficient in RAG, from setting up basic architectures to deploying scalable, real-world AI solutions!