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LLMs for Software Engineers: The Math-Free Guide to Large Language Models - Transformers, Embeddings, RAG, and Agents Explained for Developers
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Publisher: Independently Published
Pages: 98
Weight: 0.31
Height: 0.20 Width: 6.00 Depth: 9.00
Language: English
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.
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