Transformer & LLM: The Architecture Behind AI

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Transformer & LLM: The Architecture Behind AI

Author: Victor Langley

Description: Are you building with large language models but frustrated by hallucinations, context loss, and unpredictable behavior? You've read API docs and experimented with prompts, but when things go wrong, you know there's a deeper logic you're missing. Transformer & LLM: The Architecture Behind AI gives you the clear, step-by-step mental model of Transformer architecture and the LLM training pipeline that every developer needs to move beyond trial-and-error. This book is written for software developers, AI engineers, and technical founders who want to understand how modern LLMs actually work under the hood. No advanced mathematics required—just basic programming literacy and a curiosity to grasp the fundamental principles behind GPT, Claude, and other large language models. Here's what you'll learn inside: • Why language is hard for machines: Understand tokenization, subword algorithms (BPE), and how text becomes vector embeddings that capture semantic meaning. • How attention revolutionized AI: Dive into queries, keys, values, self-attention, multi-head attention, and the complete Transformer block—the architectural breakthrough that powers every major LLM. • The full training pipeline: From pretraining on massive text corpora and next-token prediction, through fine-tuning and instruction tuning, to RLHF alignment that shapes model behavior. • Building reliable applications: Master prompt engineering, context window limits, retrieval-augmented generation (RAG), tool use, function calling, and agent loops—grounded in architectural reality. • Limits and future trends: Confront hallucination, bias, safety constraints, scaling laws, and multimodal models, and learn a grounded framework for thinking about what's next. This book is perfect for developers who already use LLM APIs but want to debug prompts effectively, design robust RAG pipelines, and build AI agents that work reliably in production. It's not about prompt hacks or trend-chasing—it's about understanding the architecture so you can make informed decisions. Gain the architectural knowledge to build more reliable AI systems today.

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