Most enterprise AI doesn't fail on the model. It fails on the engineering around it. MIT's 2025 study of hundreds of deployments found that roughly 95% of enterprise generative-AI initiatives delivered no measurable return — and that the divide was driven not by model quality or regulation, but by whether the work itself was redesigned. The failures show up somewhere unglamorous: integration, messy data, evaluation, deployment, cost control, and security. Those are not artificial-intelligence problems. They are cloud and distributed-systems problems — the discipline that experienced engineers, architects, and platform teams already practise every day. Leapfrog is a vendor-neutral field guide for the engineer who is deep into a career, fluent in how real systems behave, and stuck inside a culture of "yes, but." It teaches the layer beneath the tools: how inference works, how to ground a model in your own data, how to deploy and observe a non-deterministic system, how to evaluate it, how to control its cost, and how to contain prompt injection — the layer that survives the next model release. Every chapter ends with small, concrete experiments you can ship, and with curated sources pointing to the primary research. Its central argument is simple: you were always qualified for this work. The barriers are lower than your organisation has led you to believe.