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Large Language Models and Generative AI: A Practical Guide to Understanding, Using, Building, and Evaluating Modern AI Applications
Paperback
Publisher: Independently Published
Published: Sep 4 2026
Pages: 266
Weight: 1.03
Height: 0.56 Width: 7.00 Depth: 10.00
Language: English
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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