Everything you need to know about Retrieval Augmented Generation in one human-friendly guide.
Generative AI models struggle when you ask them about facts not covered in their training data. Retrieval Augmented Generation―or RAG―enhances an LLM’s available data by adding context from an external knowledge base, so it can answer accurately about proprietary content, recent information, and even live conversations. RAG is powerful, and with A Simple Guide to Retrieval Augmented Generation, it’s also easy to understand and implement!
In A Simple Guide to Retrieval Augmented Generation you’ll learn:
The components of a RAG system
How to create a RAG knowledge base
The indexing and generation pipeline
Evaluating a RAG system
Advanced RAG strategies
RAG tools, technologies, and frameworks
A Simple Guide to Retrieval Augmented Generation shows you how to enhance an LLM with relevant data, increasing factual accuracy and reducing hallucination. Your customer service chatbots can quote your company’s policies, your teaching tools can draw directly from your syllabus, and your work assistants can access your organization’s minutes, notes, and files.
Editorial Reviews
Review
“The book does a great job of deconstructing RAG and presenting it in digestible chunks.”
Abhishek Gupta, Amazon Web Services
“A good resource for beginners and an excellent refresher for experienced people.”
Naga Santhosh Reddy Vootukuri, Senior Software Engineering Manager, Microsoft
“I can highly recommend this book. Complex topics are broken down into small and easy to understand pieces.”
Bert Gollnick, Data Scientist, Gollnick Data Solutions
From the Back Cover
From the back cover:
A Simple Guide to Retrieval Augmented Generation makes RAG simple and easy, even if you’ve never worked with LLMs before. This book goes deeper than any blog or YouTube tutorial, covering fundamental RAG concepts that are essential for building LLM-based applications. You’ll be introduced to the idea of RAG and be guided from the basics on to advanced and modularized RAG approaches―plus hands-on code snippets leveraging LangChain, OpenAI, Transformers, and other Python libraries.
Chapter-by-chapter, you’ll build a complete RAG enabled system and evaluate its effectiveness. You’ll compare and combine accuracy-improving approaches for different components of RAG, and see what the future holds for RAG. You’ll also get a sense of the different tools and technologies available to implement RAG. By the time you’re done reading, you’ll be ready to start building RAG enabled systems.
About the reader:
For data scientists, machine learning and software engineers, and technology managers who wish to build LLM-based applications. Examples in Python―no experience with LLMs necessary.
About the Author
Abhinav Kimothi is an entrepreneur and Vice President of Artificial Intelligence at Yarnit. He has spent over 15 years consulting and leadership roles in data science, machine learning and AI.
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