The Journey of Large Language Models and Retrieval-Augmented Generation Systems
Architectures and Innovations Behind GPT, LLaMA, Gemini, Claude, Falcon, DeepSeek, Qwen, Grok, and RAG Variants
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Beschreibung
This book presents a comprehensive and accessible guide to today’s most influential artificial intelligence (AI) models including GPT, LLaMA, Gemini, Claude, Falcon, DeepSeek, Qwen, Grok, and modern Retrieval-Augmented Generation (RAG) systems. Large language models (LLMs) are advanced computer programs that can understand and generate human language, and they power everyday tools such as chatbots, search engines, translation apps, and writing assistants. However, most people use these systems without knowing how they work or why different models behave differently. This book explains, in simple and clear terms, the inner machinery behind modern AI models—how they are built, trained, and improved, so that readers can better understand the technology shaping education, business, healthcare, and everyday communication. Through clear explanations, diagrams, and real-world examples, the authors demystify how these models are designed, trained, evaluated, and deployed across text, image, audio, and multimodal tasks. Ideal for students, educators, developers, and AI enthusiasts, this book bridges the gap between cutting-edge research and practical understanding, offering an essential roadmap to the rapidly evolving world of generative AI.
In addition, this book:
- Includes modern RAG architectures such as Corrective RAG, Fusion RAG, Graph RAG, Agentic RAG, Self-RAG, and Modular RAG
- Explores multimodal LLMs including image, video, and audio encoders, fusion mechanisms, and multimodal pipelines
- Studies training methodologies, alignment strategies, safety mechanisms, bias mitigation, and responsible AI deployment
This book presents a comprehensive and accessible guide to today’s most influential artificial intelligence (AI) models including GPT, LLaMA, Gemini, Claude, Falcon, DeepSeek, Qwen, Grok, and modern Retrieval-Augmented Generation (RAG) systems. Large language models (LLMs) are advanced computer programs that can understand and generate human language, and they power everyday tools such as chatbots, search engines, translation apps, and writing assistants. However, most people use these systems without knowing how they work or why different models behave differently. This book explains, in simple and clear terms, the inner machinery behind modern AI models—how they are built, trained, and improved, so that readers can better understand the technology shaping education, business, healthcare, and everyday communication. Through clear explanations, diagrams, and real-world examples, the authors demystify how these models are designed, trained, evaluated, and deployed across text, image, audio, and multimodal tasks. Ideal for students, educators, developers, and AI enthusiasts, this book bridges the gap between cutting-edge research and practical understanding, offering an essential roadmap to the rapidly evolving world of generative AI.
Includes modern RAG architectures such as Corrective RAG, Fusion RAG, Graph RAG, Agentic RAG, Self-RAG, and Modular RAG Explores multimodal LLMs including image, video, and audio encoders; fusion mechanisms; and multimodal pipelines Studies training methodologies, alignment strategies, safety mechanisms, bias mitigation, and responsible AI deployment
Autor*in
Deepshikha Bhati
Themen in »The Journey of Large Language Models and Retrieval-Augmented Generation Systems«
Large Language Model Architecture Generative AI Systems Transformer Models Retrieval-Augmented Generation Multimodal LLMs GPT vs. Gemini Claude and LLaMA Architecture DeepSeek and Qwen Models LLM training and Optimization