Building RAG Apps from Scratch

"Building RAG Apps from Scratch" is a comprehensive, practical-first guide designed to equip computer science students and developers with the skills to create sophisticated AI applications using the Retrieval-Augmented Generation (RAG) architecture. It systematically deconstructs the RAG pipeline into manageable, buildable components, focusing on implementation over abstract theory.


Philosophy

The core philosophy of this book is "Learning by Doing." In the rapidly evolving field of AI, theoretical knowledge alone is insufficient. True understanding and mastery come from hands-on implementation. This book is structured as a guided workshop, where each chapter introduces a concept and immediately follows it with practical code, real-world examples, and hands-on exercises. The objective is to transform the reader from a passive learner into an active builder of technology.


Key Features

1. Strictly Practical Focus: Over 80% of the content is dedicated to hands-on coding, implementation details, and deployment strategies.

2. Beginner to Advanced: The book starts with foundational concepts, making it accessible to beginners, but progresses to advanced architectures, optimization techniques, and deployment, providing value for advanced learners.

3. Modular Design: Each chapter focuses on a specific component of the RAG pipeline (e.g., data ingestion, vector stores, LLM integration), making it easy to learn and reference.

4. Complete Capstone Project: A full, end-to-end DIY project with complete working code and a step-by-step implementation guide in the final chapter.

5. Latest Technologies: Utilizes popular and powerful open-source libraries and frameworks such as LangChain, LlamaIndex, Hugging Face, FastAPI, and Docker.


Key Takeaways

Upon completing this book, you will be able to:

1. Design and Architect an end-to-end RAG system for any given problem.

2. Implement robust data ingestion pipelines for various document types.

3. Master the concepts of text embedding and vector databases for efficient semantic search.

4. Integrate various open-source and proprietary Large Language Models into your applications.

5. Build, Test, and Deploy a fully functional, real-world RAG application from scratch.

6. Evaluate and Optimize the performance of your RAG system using industry-standard metrics.

Disclaimer: Earnest request from the Author.

Kindly go through the table of contents and refer kindle edition for a glance on the related contents.

Thank you for your kind consideration!