Mastering Large Language Models
Год издания: 2026
Автор: Rawat A., Pathak V.
Издательство: Apress
ISBN: 979-8-8688-2733-4
Язык: Английский
Формат: PDF
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 613
Описание: This book is a hands-on guide designed to help readers understand, build, and deploy powerful AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic systems, and intelligent chatbots.
Starting with the fundamentals—LLM architecture, tokenization, APIs, and fine-tuning—the book gradually builds toward complex, integrated systems. Readers will learn to implement RAG pipelines using vector databases like FAISS and Pinecone, develop autonomous AI agents that complete multi-step tasks, and create real-world chatbots that understand and adapt to user needs. The approach is project-driven: each chapter includes visual explanations, step-by-step code walkthroughs, and deployment-ready examples. From building a personal assistant that searches your notes to creating a scheduling agent, every project reinforces both technical skills and applied understanding. It emphasizes clarity, inclusivity, and real-world relevance—helping readers move confidently from basic understanding to complex applications.
Whether you're exploring Agentic AI or looking to build production-ready systems, this book gives you the tools to turn curiosity into capability—and innovation into impact.
What you will learn:
- Build intelligent chatbots and tools using LLMs like GPT, LLaMA, and Mistral with guided development steps.
- Combine LLMs with vector databases like FAISS and Pinecone to create accurate, context-aware AI systems.
- Design AI agents capable of planning and executing complex workflows for automation and decision-making.
- Apply prompt engineering, memory, and multimodal tools to build real-world AI apps for your project portfolio.
Примеры страниц (скриншоты)
Оглавление
About the Authors xxiii
About the Technical Reviewer xxv
Acknowledgments xxvii
Introduction xxix
Part I: Foundations of LLMs 1
Chapter 1: Introduction to Large Language Models 31.1
Chapter 2: Inside the Transformer: Core Architectures 33
Chapter 3: Fine-Tuning and Alignment 77
Chapter 4: Working with LLM APIs 111
Part II: Building Intelligent Applications 157
Chapter 5: Designing Your First AI Chatbot 159
Chapter 6: Retrieval-Augmented Generation (RAG) 213
Chapter 7: RAG in Action: Personal Knowledge Search 261
Chapter 8: Agentic AI: Beyond Chatbots 307
Part III: Scaling and Deploying LLM Applications 339
Chapter 9: Project: Building an Autonomous AI Agent 341
Chapter 10: Model Serving and Inference Optimization 381
Chapter 11: Cloud, Edge, and Hybrid Deployments 415
Chapter 12: Monitoring and Observability 449
Chapter 13: Responsible AI and Safety 483
Part IV: Ethics, Governance, and the Future 517
Chapter 14: Governance, Compliance, and Security 519
Chapter 15: The Future of LLMs and Agentic AI 547
Index 581
