A Simple Guide to Retrieval Augmented Generation
暫譯: 檢索增強生成簡明指南
Kimothi, Abhinav
- 出版商: Manning
- 出版日期: 2025-07-15
- 售價: $1,700
- 貴賓價: 9.5 折 $1,615
- 語言: 英文
- 頁數: 175
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1633435857
- ISBN-13: 9781633435858
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商品描述
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. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the book 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.
- 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. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the book 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.
商品描述(中文翻譯)
關於檢索增強生成（Retrieval Augmented Generation）的所有知識，盡在這本人性化的指南中。
生成式人工智慧模型在面對訓練數據中未涵蓋的事實時，表現往往不佳。檢索增強生成（RAG）透過從外部知識庫添加上下文來增強大型語言模型（LLM）的可用數據，使其能夠準確回答有關專有內容、最新信息甚至即時對話的問題。RAG 功能強大，並且透過 檢索增強生成簡明指南，也變得易於理解和實施！ 在 檢索增強生成簡明指南 中，您將學到： - RAG 系統的組成部分- 如何創建 RAG 知識庫
- 索引和生成管道
- 評估 RAG 系統
- 進階 RAG 策略
- RAG 工具、技術和框架 檢索增強生成簡明指南 向您展示如何用相關數據增強 LLM，提高事實準確性並減少幻覺。您的客戶服務聊天機器人可以引用公司的政策，您的教學工具可以直接從課程大綱中提取內容，而您的工作助手可以訪問組織的會議記錄、筆記和文件。 購買印刷版書籍可獲得 Manning Publications 提供的免費 PDF 和 ePub 格式電子書。 關於本書 檢索增強生成簡明指南 使 RAG 變得簡單易懂，即使您從未接觸過 LLM。本書深入探討了任何部落格或 YouTube 教學所未涵蓋的基本 RAG 概念，這些概念對於構建基於 LLM 的應用至關重要。您將了解 RAG 的概念，並從基礎知識開始，逐步引導至進階和模組化的 RAG 方法，還包括利用 LangChain、OpenAI、Transformers 和其他 Python 庫的實作代碼片段。 逐章構建完整的 RAG 啟用系統並評估其有效性。您將比較和結合不同 RAG 組件的準確性提升方法，並了解 RAG 的未來發展。您還將了解可用於實施 RAG 的不同工具和技術。當您讀完這本書時，您將準備好開始構建 RAG 啟用系統。 關於讀者 本書適合希望構建基於 LLM 應用的數據科學家、機器學習和軟體工程師以及技術經理。示例使用 Python，無需具備 LLM 的經驗。 關於作者 Abhinav Kimothi 是一位企業家，並擔任 Yarnit 的人工智慧副總裁。他在數據科學、機器學習和人工智慧領域擁有超過 15 年的顧問和領導經驗。
作者簡介
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.
作者簡介(中文翻譯)
Abhinav Kimothi 是一位企業家，並擔任 Yarnit 的人工智慧副總裁。他在資料科學、機器學習和人工智慧領域擁有超過 15 年的顧問和領導經驗。
