{"product_id":"a-simple-guide-to-retrieval-augmented-generation-9781633435858","title":"A Simple Guide to Retrieval Augmented Generation","description":"\u003cb\u003eEverything you need to know about Retrieval Augmented Generation in one human-friendly guide.\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003eAugmented 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 \u003ci\u003eA Simple Guide to Retrieval Augmented Generation\u003c\/i\u003e, it's also easy to understand and implement! \u003cp\u003e\u003c\/p\u003eIn \u003ci\u003eA Simple Guide to Retrieval Augmented Generation\u003c\/i\u003e you'll learn: \u003cp\u003e\u003c\/p\u003e- The components of a RAG system\u003cbr\u003e - How to create a RAG knowledge base\u003cbr\u003e - The indexing and generation pipeline\u003cbr\u003e - Evaluating a RAG system\u003cbr\u003e - Advanced RAG strategies\u003cbr\u003e - RAG tools, technologies, and frameworks \u003cp\u003e\u003c\/p\u003e\u003ci\u003eA Simple Guide to Retrieval Augmented Generation\u003c\/i\u003e 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. \u003cp\u003e\u003c\/p\u003e\u003cb\u003eAbout the Technology\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003eIf 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. \u003cp\u003e\u003c\/p\u003e\u003cb\u003eAbout the Book\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e\u003ci\u003eA Simple Guide to Retrieval Augmented Generation\u003c\/i\u003e 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! \u003cp\u003e\u003c\/p\u003e\u003cb\u003eWhat's Inside\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e- RAG components and applications\u003cbr\u003e - Evaluating RAG systems\u003cbr\u003e - Tools and frameworks for implementing RAG \u003cp\u003e\u003c\/p\u003e\u003cb\u003eAbout the Readers\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003eFor data scientists, engineers, and technology managers--no prior LLM experience required. Examples use simple, well-annotated Python code. \u003cp\u003e\u003c\/p\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e\u003cb\u003eAbhinav Kimothi\u003c\/b\u003e is a seasoned data and AI professional. He has spent over 15 years in consulting and leadership roles in data science, machine learning and AI, and currently works as a Director of Data Science at Sigmoid. \u003cp\u003e\u003c\/p\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003ePart 1\u003cbr\u003e 1 LLMs and the need for RAG\u003cbr\u003e 2 RAG systems and their design\u003cbr\u003e Part 2\u003cbr\u003e 3 Indexing pipeline: Creating a knowledge base for RAG\u003cbr\u003e 4 Generation pipeline: Generating contextual LLM responses\u003cbr\u003e 5 RAG evaluation: Accuracy, relevance, and faithfulness\u003cbr\u003e Part 3\u003cbr\u003e 6 Progression of RAG systems: Naïve, advanced, and modular RAG\u003cbr\u003e 7 Evolving RAGOps stack\u003cbr\u003e Part 4\u003cbr\u003e 8 Graph, multimodal, agentic, and other RAG variants\u003cbr\u003e 9 RAG development framework and further exploration \u003cp\u003e\u003c\/p\u003eGet a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eAbhinav Kimothi\u003c\/b\u003e 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.\u003cbr\u003e","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":51580117483794,"sku":"9781633435858","price":45.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0831\/4771\/8930\/files\/img_aa42361e-cf62-4655-81de-116fb13c4682.jpg?v=1756206044","url":"https:\/\/surprise-castle.myshopify.com\/products\/a-simple-guide-to-retrieval-augmented-generation-9781633435858","provider":"Surprise Castle","version":"1.0","type":"link"}