RAG-Driven Generative AI

Build custom retrieval augmented generation pipelines with LlamaIndex, Deep Lake, and Pinecone

Author: Denis Rothman

Explore the transformative potential of RAG-driven LLMs, computer vision, and generative AI, from basics to building a complex RAG pipeline

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Summary

Explore the transformative potential of RAG-driven LLMs, computer vision, and generative AI, from basics to building a complex RAG pipeline

Description

Minimize AI hallucinations and build accurate, custom generative AI pipelines with RAG using embedded vector databases and integrated human feedbackPurchase of the print or Kindle book includes a free eBook in PDF formatKey FeaturesImplement RAG’s traceable outputs, linking each response to its source document to build reliable multimodal conversational agentsDeliver accurate generative AI models in pipelines integrating RAG, real-time human feedback improvements, and knowledge graphsBalance cost and performance between dynamic retrieval datasets and fine-tuning static dataBook DescriptionRAG-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. You’ll discover techniques to optimize your project’s 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.You’ll 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.What you will learnScale RAG pipelines to handle large datasets efficientlyEmploy techniques that minimize hallucinations and ensure accurate responsesImplement indexing techniques to improve AI accuracy with traceable and transparent outputsCustomize and scale RAG-driven generative AI systems across domainsFind out how to use Deep Lake and Pinecone for efficient and fast data retrievalControl and build robust generative AI systems grounded in real-world dataCombine text and image data for richer, more informative AI responsesWho this book is forThis 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 you’ll find this book useful.

About the Author

Denis Rothman graduated from Sorbonne University and Paris-Diderot University, and as a student, he wrote and registered a patent for one of the earliest word2vector embeddings and word piece tokenization solutions. He started a company focused on deploying AI and went on to author one of the first AI cognitive NLP chatbots, applied as a language teaching tool for Moët et Chandon (part of LVMH) and more. Denis rapidly became an expert in explainable AI, incorporating interpretable, acceptance-based explanation data and interfaces into solutions implemented for major corporate projects in the aerospace, apparel, and supply chain sectors. His core belief is that you only really know something once you have taught somebody how to do it.