LLMs for Software Engineers : The Math-Free Guide to Large Language Models - Transformers, Embeddings, RAG, and Agents Explained for Developers
Language: English
Published by Amazon Digital Services LLC - Kdp Sep 2026, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
US$ 25.38
Quantity: 2 available
Add to basketItem description from seller
Neuware - You know how to write software. You don't need another AI book that treats you like a ChatGPT tourist - and you definitely don't need one that opens with linear algebra.LLMs for Software Engineers is the missing middle: a complete, math-free explanation of how large language models actually work, written specifically for developers who want to build real applications with them - not derive backpropagation by hand.Using analogies you already understand from years of writing software - database joins, caching, lexers, hash maps, distributed systems - this book gives you genuine, durable mental models for the concepts every AI-adjacent engineering role now assumes you know: - How tokens, embeddings, and the transformer's attention mechanism actually work - no calculus required- The real difference between training and inference, and why a model can't 'just learn' from your conversation- How to build Retrieval-Augmented Generation (RAG) systems that ground LLM output in your own data- When to reach for prompting, RAG, or fine-tuning - and why treating them as interchangeable wastes time and money- How agents and function calling actually work under the hood, and how to build them reliably- Why hallucinations happen, and the concrete engineering mitigations that actually reduce them- How to evaluate LLM outputs, manage cost and latency, and defend against prompt injection in production systemsEvery chapter ends with a practical 'What This Means for You' takeaway, and a full glossary at the back means you can use this book as a working reference long after you've read it cover to cover.If you've been putting off learning 'the AI stuff' because every resource you've found is either a ChatGPT tutorial or a machine learning PhD textbook, this is the book that was actually missing.…
Seller Inventory # 9798171229702
- Title
- LLMs for Software Engineers : The Math-Free Guide to Large Language Models - Transformers, Embeddings, RAG, and Agents Explained for Developers
- Author
- Ft Styles
- Publisher
- Amazon Digital Services LLC - Kdp Sep 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798171229702
- Item weight
- 141 grams
- Dimensions
- 229x152x5 mm
You know how to write software. You don't need another AI book that treats you like a ChatGPT tourist — and you definitely don't need one that opens with linear algebra.
LLMs for Software Engineers is the missing middle: a complete, math-free explanation of how large language models actually work, written specifically for developers who want to build real applications with them — not derive backpropagation by hand.
Using analogies you already understand from years of writing software — database joins, caching, lexers, hash maps, distributed systems — this book gives you genuine, durable mental models for the concepts every AI-adjacent engineering role now assumes you know:
- How tokens, embeddings, and the transformer's attention mechanism actually work — no calculus required
- The real difference between training and inference, and why a model can't "just learn" from your conversation
- How to build Retrieval-Augmented Generation (RAG) systems that ground LLM output in your own data
- When to reach for prompting, RAG, or fine-tuning — and why treating them as interchangeable wastes time and money
- How agents and function calling actually work under the hood, and how to build them reliably
- Why hallucinations happen, and the concrete engineering mitigations that actually reduce them
- How to evaluate LLM outputs, manage cost and latency, and defend against prompt injection in production systems
Every chapter ends with a practical "What This Means for You" takeaway, and a full glossary at the back means you can use this book as a working reference long after you've read it cover to cover.
If you've been putting off learning "the AI stuff" because every resource you've found is either a ChatGPT tutorial or a machine learning PhD textbook, this is the book that was actually missing.
"Synopsis" may belong to another edition of this title.
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