Name: LLMs for Software Engineers: The Math-Free Guide to Large Language Models — Transformers, Embeddings, RAG, and Agents Explained for Developers
SKU: 68908048
Price: 19.71 EUR
Availability: InStock
Author: Styles, FT
ISBN: 9798171229702

portada LLMs for Software Engineers: The Math-Free Guide to Large Language Models — Transformers, Embeddings, RAG, and Agents Explained for Developers

LLMs for Software Engineers: The Math-Free Guide to Large Language Models — Transformers, Embeddings, RAG, and Agents Explained for Developers

Type

Physical Book

Publisher

Independently published

Author

Styles, FT

Format

Paperback

ISBN13

9798171229702

LLMs for Software Engineers: The Math-Free Guide to Large Language Models — Transformers, Embeddings, RAG, and Agents Explained for Developers

Styles, Ft (Author) · Independently published · Paperback

New Book Imported to Netherlands

Delivery: 13 Oct - 15 Oct

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Synopsis "LLMs for Software Engineers: The Math-Free Guide to Large Language Models — Transformers, Embeddings, RAG, and Agents Explained for Developers"

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.