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Why does a modern AI system need retrieval, tools, agents, a harness, and evals?
Each exists because something before it broke.
How LLMs Happened follows the chain of ideas that led from attention and the Transformer to today's large language model systems. It covers scaling, training data, alignment, prompting, retrieval-augmented generation, fine-tuning, function calling, agents, MCP, multimodality, reasoning, smaller models, harnesses, and evaluations.
Tok and Professor Atlas guide readers through sixteen illustrated chapters. Every chapter solves one problem and reveals the next, allowing the modern LLM stack to form as one continuous story rather than a disconnected list of technical terms.
This book is for engineers beginning to work with LLMs, technical product managers, founders, students, and curious readers who want to understand why these technologies developed without starting with heavy mathematics.
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