# AI-Assisted Programming: Better Planning, Coding, Testing, and Deployment by [[Tom Taulli]] ## Summary <!-- a couple of paragraphs --> Tom Taulli's *AI-Assisted Programming* surveys how AI coding tools reshape the full software development lifecycle — not just writing code, but planning, requirements gathering, testing, and deployment. It introduces the major assistants (such as GitHub Copilot and ChatGPT) and shows how to integrate them into everyday developer workflows, with attention to the prompt techniques that get useful output from them. The book is organized around the phases of development, treating requirements and planning as a first-class concern where AI helps draft and refine specifications. It covers prompt engineering as the core skill for steering these tools, and devotes attention to using AI to generate and strengthen tests, including behavior expressed in Gherkin-style scenarios. Throughout, Taulli balances enthusiasm with the practical limits and risks of relying on generated code. ## Table of Contents - Ch. 1 — What Is AI-Assisted Programming? - Ch. 2 — Prompt Engineering - Ch. 3 — Requirements and Planning - Ch. 4 — GitHub Copilot - Ch. 5 — ChatGPT and Other Tools - Testing (unit tests, Gherkin scenarios) - Deployment and DevOps ## Notes <!-- main takeaways; LINK to the permanent notes this book grounds --> - Grounds requirements and planning, including the [[Software Requirements Specification]]. - Backs [[Gherkin Scenario]] as a way to express AI-generated test behavior. - Primary practical grounding for [[Prompt Engineering]] as the core steering skill. ## Quotes - <!-- placeholder: add a verified short quote here --> ## Relevance to the course - Grounds Module 3 (requirements, planning, executable scenarios) and Module 8 (prompt engineering for AI-assisted development). --- ## References -