A Simple Guide to Retrieval Augmented Generation的封面图片
A Simple Guide to Retrieval Augmented Generation

A Simple Guide to Retrieval Augmented Generation

图书出版业

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

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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 systems 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. 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.

网站
https://mng.bz/EZor
所属行业
图书出版业
规模
1 人
类型
个体经营
创立
2024
领域
Generative AI、Retrieval Augmented Generation、LLMs、Machine Learning和Artificial Intelligence

动态

  • A Simple Guide to Retrieval Augmented Generation转发了

    I am absolutely chuffed to announce that all chapters of A Simple Guide to Retrieval Augmented Generation have finally been released. The book is a foundational guide for individuals looking to explore Retrieval Augmented Generation. This book is for technology professionals who want to get introduced to the concept of Retrieval Augmented Generation and build LLM-based apps. It will prove to be a handy book for beginners as well as experienced professionals. You'll also get an opportunity to code along in python but the book is intended for non-coders as well. From introducing the technique to building RAG pipelines in production, in 9 chapters the book covers the following - Chapter 1?- Large Language Models and the Need for Retrieval Augmented Generation Chapter 2?- RAG systems and their design Chapter 3?- Indexing Pipeline : Creating a knowledge base for RAG based applications Chapter 4 - Generation Pipeline: Real time interaction for contextual responses Chapter 5 - RAG Evaluation : Checking accuracy, relevance and faithfulness Chapter 6 - Evolving RAGOps Stack : Technologies that make RAG possible Chapter 7 - Progression of RAG systems : Naive to Advanced to Modular Chapter 8 - Rag variants: Multimodal, agentic, graph and other rags Chapter 9 - RAG Development Framework & areas of further exploration If you haven't already, please get your copy here - https://mng.bz/jXJ9 If you like to get hands-on, the GitHub repo of the book is public. You can star/clone/fork it here: https://lnkd.in/gRVSp7mC I'm Abhinav, if you have any questions or observations, please let me know your comments.

  • A Simple Guide to Retrieval Augmented Generation转发了

    Generative AI, Large Language Models, Retrieval Augmented Generation, AI Agents are most likely going to be the buzz words in 2025. If you are planning to build contemporary AI applications, it is unlikely that you are going to use traditional search methods. To meet the evolving user needs of accuracy, personalisation, and relevance, modern search and retrieve features need to be guided by AI too. AI-Powered Search by Trey Grainger, Doug Turnbull and Max Irwin, published by Manning Publications Co. is an incredible guide to design and implement modern search systems. Key Highlights - Modern search systems like RAG need to be domain-aware, personalised, contextual, and even conversational. The book addresses these needs by integrating semantic search, knowledge graphs, and foundation models. - The accompanying code repository gives a necessary practical focus to the theory of AI driven search. - The book also dives into techniques for scalable indexing, query processing, and retrieval methods, ensuring systems handle large-scale data effectively. - It combines traditional search algorithms (TF-IDF, BM25) with deep learning approaches like transformer models, dense embeddings, and fine-tuned LLMs, offering a hybrid model for superior performance. - It also provides the necessary details about emerging paradigms like multimodal search, generative search, and agent-based search. If you're a data scientist, a software developer, a business leader or a product manager building search engines, chatbots, or intelligent assistants, this book equips you with the knowledge and code to deliver state-of-the-art solutions. You can get your copy here - https://mng.bz/8OXB Thanks Aira Ducic for introducing me to this wonderful resource.

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  • A Simple Guide to Retrieval Augmented Generation转发了

    Based on a survey of Indian VCs by Inc42 Media, 46% Of Indian VCs Are Pushing For Startups To Focus On Retrieval-Augmented Generation (RAG) Solutions One of the quickest ways to learn a concept is to study the common questions and their answers around the subject. This book contains a list of 80 questions divided in 8 groups for a quick, in-depth understanding of the technique. Here are a set of 40 questions on RAG foundations from Around RAG in 80 Questions based on A Simple Guide to Retrieval Augmented Generation Do let me know what you think? Are there any other questions you think are important to gain a foundational understanding of RAG? PS - If you're don't want to spend money and get the entire ebook, do send me a DM.

  • A Simple Guide to Retrieval Augmented Generation转发了

    Based on my book A Simple Guide to Retrieval Augmented Generation (https://lnkd.in/gcDAGucF), I have put together 80 questions that will help in getting a quick understanding of the key concepts of RAG. It has 8 sections based on the first 8 chapters of the book. ??RAG Basics: Questions on limitations of LLMs and the introduction to RAG ??RAG System Design: Questions on the high level design of RAG systems ??Indexing Pipeline: Questions on creating a knowledge brain for RAG systems ??Generation Pipeline: Questions on real-time user interaction in RAG systems ??Evaluation:Questions on the evaluation of RAG systems ??Beyond Naive RAG: Questions on advanced RAG techniques ??Evolving RAGOps Stack: Questions on the technology stack for RAG ??RAG Variants: Questions on RAG patterns like multimodal, graph, agents, etc. Attached are ten questions on Chapter 1 - LLMs and the need for RAG. I call this project Around RAG in 80 Questions ?? In case you want to purchase the complete e-book with all the questions and answers, you can download it from Gumroad - https://lnkd.in/gY6xkjGZ I will continue to share the contents in subsequent posts so if you don't want to download the ebook right away, you can follow me and watch this space for more. (In case, you're looking for a discount, please send me a message) What do you think? What other questions should be a part of this?

  • A Simple Guide to Retrieval Augmented Generation转发了

    Retrieval Augmented Generation, or RAG, stands as a pivotal technique shaping the landscape of applied generative AI. A novel concept introduced by Lewis et. al., in their seminal paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, RAG has swiftly emerged as a cornerstone, enhancing reliability and trustworthiness in the outputs from Large Language Models (LLMs). A few months ago, Ali Ghodsi, co-founder and CEO of Databricks, revealed that their customers are actively embracing RAG, with 60% of their use cases involving LLMs being built upon this architecture. For developers, managers and business leaders, RAG has become as essential a concept to understand as Generative AI and Large Language Models. One of the quickest ways to learn a concept is to study the common questions and their answers around the subject. So, I put together 80 questions that will help in getting a quick understanding of the key concepts of RAG. It has 8 sections - ??RAG Basics: Questions on limitations of LLMs and the introduction to RAG ??RAG System Design: Questions on the high level design of RAG systems ??Indexing Pipeline: Questions on creating a knowledge brain for RAG systems ??Generation Pipeline: Questions on real-time user interaction in RAG systems ??Evaluation:Questions on the evaluation of RAG systems ??Beyond Naive RAG: Questions on advanced RAG techniques ??Evolving RAGOps Stack: Questions on the technology stack for RAG ??RAG Variants: Questions on RAG patterns like multimodal, graph, agents, etc. These questions are based on my book A Simple Guide to Retrieval Augmented Generation. Please consider purchasing a copy - https://lnkd.in/gcDAGucF I am attaching all the questions here. I call this project Around RAG in 80 Questions ?? In case you want to purchase the complete e-book with all the questions and answers, you can download it from Gumroad - https://lnkd.in/gY6xkjGZ I will continue to share the contents in subsequent posts so if you don't want to download the ebook right away, you can follow me and watch this space for more. (In case, you're looking for a discount, please send me a message) What do you think? What other questions should be a part of this?

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