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Denis Rothman
RAG-Driven Generative AI
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4996959RAG-Driven Generative AIhttps://www.gandhi.com.mx/rag-driven-generative-ai-9781836200901/phttps://gandhi.vtexassets.com/arquivos/ids/4547591/image.jpg?v=638889879650470000650722MXNPackt PublishingInStock/Ebooks/4731959RAG-Driven Generative AI650722https://www.gandhi.com.mx/rag-driven-generative-ai-9781836200901/phttps://gandhi.vtexassets.com/arquivos/ids/4547591/image.jpg?v=638889879650470000InStockMXN99999DIEbook20249781836200901_W3siaWQiOiJkZTA0MDk5NC0wNThlLTQ5YTAtYjAxNS03MjI3ZTNmOWIyZmEiLCJsaXN0UHJpY2UiOjcyMiwiZGlzY291bnQiOjcyLCJzZWxsaW5nUHJpY2UiOjY1MCwiaW5jbHVkZXNUYXgiOnRydWUsInByaWNlVHlwZSI6Ildob2xlc2FsZSIsImN1cnJlbmN5IjoiTVhOIiwiZnJvbSI6IjIwMjQtMDktMzBUMDA6MDA6MDBaIiwicmVnaW9uIjoiTVgiLCJpc1ByZW9yZGVyIjpmYWxzZX1d9781836200901_<p><b>Minimize hallucinations and build accurate, custom Generative AI pipelines with RAG using embedded vector databases and integrated human feedback Purchase of the print or Kindle book includes a free eBook in PDF format</b></p><h2>Key Features</h2><ul><li>Implement RAGs traceable outputs, linking each response to its source document to build reliable multimodal conversational agents</li><li>Deliver accurate generative AI models in pipelines integrating RAG, real-time human feedback improvements, and indexed embedded knowledge graphs</li><li>Balance cost and performance between dynamic retrieval datasets and fine-tuning static data</li></ul><h2>Book Description</h2>Designing and managing controlled, reliable multimodal generative AI pipelines is complex. RAG-Driven Generative AI provides a roadmap for building effective LLM, computer vision, and Generative AI systems that will balance performance and costs. From foundational concepts to complex implementations, this book offers a detailed exploration of how RAG can control and enhance AI systems by tracing each output to its source document. RAGs traceable process allows human feedback for continual improvements, minimizing inaccuracies, hallucinations, and bias. This AI book shows you how to build a RAG framework from scratch, providing practical knowledge on vector stores, chunking, indexing, and ranking. Youll discover techniques to optimize performance and costs, improving model accuracy by combining with human feedback, managing costs with when to fine-tune, and improving accuracy and retrieval speed by combining with embedded-indexed knowledge graphs. Experience a blend of theory and practice using frameworks like LlamaIndex, LangChain, Pinecone, and Deep Lake and models from Hugging Face, OpenAI, and Google Vertex AI. By the end of this book, you will have acquired the skills to implement intelligent solutions, keeping you competitive in fields from production to customer service across any project.<h2>What you will learn</h2><ul><li>Mitigate common AI challenges, such as bias, hallucinations, and misinformation</li><li>Integrate computer vision for multimodal search</li><li>Customize and scale RAG-driven Generative AI systems across various domains</li><li>Deep dive into Metas RAG model for question-answering</li><li>Learn to control and build robust Generative AI systems grounded in real-world data</li><li>Build RAG-driven LLM and computer vision pipelines from design to large-scale implementation</li></ul><h2>Who this book is for</h2><p>This book is ideal for data scientists, AI engineers, machine learning engineers, MLOps engineers, as well as solution architects, software developers, and product and project managers working on LLM and computer vision projects who want to learn and apply RAG for real-world applications. Researchers and natural language processing practitioners working with large language models and text generation will also find the book useful</p>...(*_*)9781836200901_<p><b>Minimize AI hallucinations and build accurate, custom generative AI pipelines with RAG using embedded vector databases and integrated human feedback Purchase of the print or Kindle book includes a free eBook in PDF format</b></p><h2>Key Features</h2><ul><li>Implement RAGs traceable outputs, linking each response to its source document to build reliable multimodal conversational agents</li><li>Deliver accurate generative AI models in pipelines integrating RAG, real-time human feedback improvements, and knowledge graphs</li><li>Balance cost and performance between dynamic retrieval datasets and fine-tuning static data</li></ul><h2>Book Description</h2>RAG-Driven Generative AI provides a roadmap for building effective LLM, computer vision, and generative AI systems that balance performance and costs. This book offers a detailed exploration of RAG and how to design, manage, and control multimodal AI pipelines. By connecting outputs to traceable source documents, RAG improves output accuracy and contextual relevance, offering a dynamic approach to managing large volumes of information. This AI book shows you how to build a RAG framework, providing practical knowledge on vector stores, chunking, indexing, and ranking. Youll discover techniques to optimize your projects performance and better understand your data, including using adaptive RAG and human feedback to refine retrieval accuracy, balancing RAG with fine-tuning, implementing dynamic RAG to enhance real-time decision-making, and visualizing complex data with knowledge graphs. Youll be exposed to a hands-on blend of frameworks like LlamaIndex and Deep Lake, vector databases such as Pinecone and Chroma, and models from Hugging Face and OpenAI. By the end of this book, you will have acquired the skills to implement intelligent solutions, keeping you competitive in fields from production to customer service across any project.<h2>What you will learn</h2><ul><li>Scale RAG pipelines to handle large datasets efficiently</li><li>Employ techniques that minimize hallucinations and ensure accurate responses</li><li>Implement indexing techniques to improve AI accuracy with traceable and transparent outputs</li><li>Customize and scale RAG-driven generative AI systems across domains</li><li>Find out how to use Deep Lake and Pinecone for efficient and fast data retrieval</li><li>Control and build robust generative AI systems grounded in real-world data</li><li>Combine text and image data for richer, more informative AI responses</li></ul><h2>Who this book is for</h2><p>This book is ideal for data scientists, AI engineers, machine learning engineers, and MLOps engineers. If you are a solutions architect, software developer, product manager, or project manager looking to enhance the decision-making process of building RAG applications, then youll find this book useful.</p>...(*_*)9781836200901_<p><b>Minimize AI hallucinations and build accurate, custom generative AI pipelines with RAG using embedded vector databases and integrated human feedback Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free</b></p><h2>Key Features</h2><ul><li>Implement RAGs traceable outputs, linking each response to its source document to build reliable multimodal conversational agents</li><li>Deliver accurate generative AI models in pipelines integrating RAG, real-time human feedback improvements, and knowledge graphs</li><li>Balance cost and performance between dynamic retrieval datasets and fine-tuning static data</li></ul><h2>Book Description</h2>RAG-Driven Generative AI provides a roadmap for building effective LLM, computer vision, and generative AI systems that balance performance and costs. This book offers a detailed exploration of RAG and how to design, manage, and control multimodal AI pipelines. By connecting outputs to traceable source documents, RAG improves output accuracy and contextual relevance, offering a dynamic approach to managing large volumes of information. This AI book shows you how to build a RAG framework, providing practical knowledge on vector stores, chunking, indexing, and ranking. Youll discover techniques to optimize your projects performance and better understand your data, including using adaptive RAG and human feedback to refine retrieval accuracy, balancing RAG with fine-tuning, implementing dynamic RAG to enhance real-time decision-making, and visualizing complex data with knowledge graphs. Youll be exposed to a hands-on blend of frameworks like LlamaIndex and Deep Lake, vector databases such as Pinecone and Chroma, and models from Hugging Face and OpenAI. By the end of this book, you will have acquired the skills to implement intelligent solutions, keeping you competitive in fields from production to customer service across any project.<h2>What you will learn</h2><ul><li>Scale RAG pipelines to handle large datasets efficiently</li><li>Employ techniques that minimize hallucinations and ensure accurate responses</li><li>Implement indexing techniques to improve AI accuracy with traceable and transparent outputs</li><li>Customize and scale RAG-driven generative AI systems across domains</li><li>Find out how to use Deep Lake and Pinecone for efficient and fast data retrieval</li><li>Control and build robust generative AI systems grounded in real-world data</li><li>Combine text and image data for richer, more informative AI responses</li></ul><h2>Who this book is for</h2><p>This book is ideal for data scientists, AI engineers, machine learning engineers, and MLOps engineers. If you are a solutions architect, software developer, product manager, or project manager looking to enhance the decision-making process of building RAG applications, then youll find this book useful.</p>...9781836200901_Packt Publishinglibro_electonico_9781836200901_9781836200901Denis RothmanInglésMéxico2024-09-30T00:00:00+00:00https://getbook.kobo.com/koboid-prod-public/packt-epub-47bf9ee6-cfc1-4be8-99f6-3cc5b5ab95de.epub2024-09-30T00:00:00+00:00Packt Publishing