Paperback. Most AI tutorials stop at "look, it works." This book keeps going.Large Language Models and Generative AI is a hands-on guide to building real AI applications with Python - not toy demos, but systems that retrieve real information, call real tools, run real agents, and hold up under real users.You'll start with a single API call to an LLM and build outward, chapter by chapter: structured prompts, embeddings and RAG for grounding answers in your own data, tool calling and multi-step workflows, autonomous agents with memory and planning, and fine-tuning models to run locally. Then - where most books stop - you'll learn to evaluate what you've built, secure it against prompt injection and data leaks, and deploy it as a monitored, production-ready service.Every chapter closes with a project you actually build: a private knowledge assistant, a tool-using AI agent, a research agent, a security-tested application, and a complete generative AI system that ties everything together.What you'll walk away with: A working model of the full LLM application stack: prompts, RAG, tools, agents, evaluation, and securityPractical experience with embeddings, vector search, and retrieval-augmented generationThe ability to build and evaluate AI agents that plan, use tools, and hold stateFine-tuning and local model deployment with LoRA and QLoRAProduction skills: FastAPI backends, Docker deployment, cost control, and observabilityTen complete, working projects you can extend for your own portfolio or productWhy this book is different: most AI books teach you to prompt a model and call it finished. This one treats evaluation and security as core chapters, not afterthoughts - because an AI application nobody has tested and nobody has secured isn't done. It's just untested.Whether you're a developer moving into AI, an engineer tired of brittle demos, or someone who wants to ship something that actually works, this book maps the full path from first API call to production deployment.Scroll up and grab your copy to start building AI applications that go the distance. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

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Bibliographic Details

Title: Large Language Models and Generative AI (Paperback)
Publisher: Independently Published
Publication Date: 2026
Language: English
ISBN 13: 9798171920784
Binding: Paperback
Condition: new

About this title

Synopsis

Most AI tutorials stop at "look, it works." This book keeps going.

Large Language Models and Generative AI is a hands-on guide to building real AI applications with Python - not toy demos, but systems that retrieve real information, call real tools, run real agents, and hold up under real users.

You'll start with a single API call to an LLM and build outward, chapter by chapter: structured prompts, embeddings and RAG for grounding answers in your own data, tool calling and multi-step workflows, autonomous agents with memory and planning, and fine-tuning models to run locally. Then - where most books stop - you'll learn to evaluate what you've built, secure it against prompt injection and data leaks, and deploy it as a monitored, production-ready service.

Every chapter closes with a project you actually build: a private knowledge assistant, a tool-using AI agent, a research agent, a security-tested application, and a complete generative AI system that ties everything together.

What you'll walk away with:

A working model of the full LLM application stack: prompts, RAG, tools, agents, evaluation, and security
Practical experience with embeddings, vector search, and retrieval-augmented generation
The ability to build and evaluate AI agents that plan, use tools, and hold state
Fine-tuning and local model deployment with LoRA and QLoRA
Production skills: FastAPI backends, Docker deployment, cost control, and observability
Ten complete, working projects you can extend for your own portfolio or product

Why this book is different: most AI books teach you to prompt a model and call it finished. This one treats evaluation and security as core chapters, not afterthoughts - because an AI application nobody has tested and nobody has secured isn't done. It's just untested.

Whether you're a developer moving into AI, an engineer tired of brittle demos, or someone who wants to ship something that actually works, this book maps the full path from first API call to production deployment.

Scroll up and grab your copy to start building AI applications that go the distance.

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