Your team bought the tool. Writing the code got thirty times faster. Delivery got 1.7 times faster. That gap is not a tooling problem, and no amount of additional model capability will close it. It is what happens when one stage of a lifecycle is transformed and the other five are left exactly as they were. Understanding why is the difference between an engineering organization that gets value from these systems and one that has expensive licenses and the same release cadence as last year. Practical AI with Claude and AWS is a field guide for the whole engineering organization, not a specialist corner of it. It teaches the mental models that survive the churn: what language models actually do, where they belong in a system, how to build with them, and what it takes to run the result in production. It is deliberately not a reference manual. APIs change; the reasoning behind a design does not. Every chapter is written to stay useful after the version numbers move, and points you at current documentation where currency actually matters. What's inside Part I — Foundations. How models behave and why they fail. Machine learning fundamentals without the mathematics, how large language models actually work, and prompting as specification writing rather than incantation. Part II — Building with Models. Giving models tools. Skills and progressive disclosure. The workflow-versus-agent decision and why most systems should not be agents. Orchestration patterns, retrieval-augmented generation, fine-tuning, and how to choose between them. Part III — Production and Delivery. Where agents run. Infrastructure as code. Observability for non-deterministic systems. Evaluation that catches regressions before users do. Security, including the failure modes that have no equivalent in conventional software. Cost control. And a chapter on the AI-native development lifecycle — how the stages nobody has touched become the constraint once code generation is solved. Two complete systems, worked end to end, plus a decision cheat sheet and a full glossary. Who this is for Engineers who need to build something real, not a demo Architects deciding where these systems belong and where they do not Technical leaders who have to justify the spend and answer for the risk Anyone in a regulated environment who needs an audit trail, not just an output Prerequisites: General software literacy. No mathematics. No prior machine learning. Examples use Claude and AWS because that is a stack you can actually deploy on, but the reasoning transfers. Where a decision is provider-specific, the book says so. Part of a series of field guides for engineering teams. This is an independent publication and is not affiliated with, authorized by, endorsed by, or sponsored by Anthropic or Amazon Web Services.
