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An A-to-Z roadmap on implementing Retrieval Augmented Generation for LLM AI systems Practical Retrieval Augmented Generation: Building Context-Aware AI Systems, by Harpreet Sahota, is a timely and authoritative discussion of how to use Retrieval Augmented Generation (RAG) to enhance the utility and efficacy of Large Language Model (LLM) deployments within your company. This book is a must-read for technical team leads, managers, or executives looking to leverage RAG's capabilities to make AI systems more effective in various use cases, including text, video, and audio interactions. This book is an easy-to-understand technical explanation of RAG systems, from overviews to specifics on working with vector databases, document chunking strategies, vector search and retrieval, response synthesis, and more. Inside, you will discover:
- Detailed Preparation Techniques: Learn how to prepare and ingest diverse data types into a vector database, setting the foundation for robust RAG applications.
- From Basic to Advanced RAG: Explore a spectrum of simple to advanced techniques for integrating RAG in both text-only and multimodal contexts.
- RAG Evaluation: Gain insights into effective methods for assessing the performance of your RAG systems to ensure they meet operational needs.
- Real-World Applications: Through practical applications and case studies, see how companies have successfully implemented RAG systems to enhance the utility of their LLM deployments.
- Build and Share Your Prototype: Step-by-step guidance on creating a simple demo
Practical Retrieval Augmented Generation is a can't-miss resource for software engineers, data scientists, and other technical professionals who seek a start-to-finish understanding of AI Engineering and RAG deployments.
