A Simple Guide to Retrieval Augmented Generation, Video Edition

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[center][b]A Simple Guide to Retrieval Augmented Generation, Video Edition [Update 06/2025][/b]
English | Size: 1.1 GB
Genre: eLearning[/center]

In Video Editions the narrator reads the book while the content, figures, code listings, diagrams, and text appear on the screen. Like an audiobook that you can also watch as a video.

Everything you need to know about Retrieval Augmented Generation in one human-friendly guide.

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 gives an easy, yet comprehensive, introduction to RAG for AI beginners. You'll go from basic RAG that uses indexing and generation pipelines, to modular RAG and multimodal data from images, spreadsheets, and more.

About the Technology
If you want to use a large language model to answer questions about your specific business, you're out of luck. The LLM probably knows nothing about it and may even make up a response. Retrieval Augmented Generation is an approach that solves this class of problems. The model first retrieves the most relevant pieces of information from your knowledge stores (search index, vector database, or a set of documents) and then generates its answer using the user's prompt and the retrieved material as context. This avoids hallucination and lets you decide what it says.

About the Book
A Simple Guide to Retrieval Augmented Generation is a plain-English guide to RAG. The book is easy to follow and packed with realistic Python code examples. It takes you concept-by-concept from your first steps with RAG to advanced approaches, exploring how tools like LangChain and Python libraries make RAG easy. And to make sure you really understand how RAG works, you'll build a complete system yourself-even if you're new to AI!

What's Inside
RAG components and applications
Evaluating RAG systems
Tools and frameworks for implementing RAG

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