AI駆動開発 spans 設計 to テスト, not just code

The article's starting point is a familiar imbalance: most teams adopt generative AI for code generation, which is the most obvious use, but that limits the benefit to the implementation stage. Its proposal is to treat AI as a helper in every stage of a conventional sequence — requirements, design, implementation, debugging, testing, review, documentation — without claiming those stages disappear.

The practical weight falls on preparation rather than prompting. The article warns that stale specs, contradictory requirements, unnecessary confidential information or bad sample code in the input will shape the output, and that vague correctness criteria make AI output impossible to judge. It points to concrete evaluation tools already common in engineering: acceptance conditions, type checks, Lint, unit and integration tests, security inspection and human review. More input is not automatically better; irrelevant or conflicting material can obscure the premises.

For readers outside engineering, the notable claim is organizational rather than technical. The article positions AI as a generator of options and questions, and humans as the ones who accept risk, decide architecture, and approve generated code. How much this shortens real cycles likely hinges on whether teams already have written acceptance criteria and test cases to hand an AI — a capability that varies widely across organizations.