AIに「それっぽいコード」で逃げられないためのプロンプト設計｜設計規約〜実装〜テストまでAI主導で回した知見Based on hands-on experience driving the full development cycle—design rules,…

Driving software development with AI coding assistants has moved from novelty to routine for many engineers, and a recent practitioner's account on Qiita examines a subtle trap that comes with the shift. The author describes running an entire workflow—from authoring design conventions through implementation planning to coding and writing test code—with Cursor at the center, and concludes that the common assumption "hand the AI the rules and you're done" turns out to be naive. The point matters because AI-led development is increasingly presented as a matter of feeding a model good documentation, and this experience suggests that framing understates the effort required to get reliable results.

The piece is organized around two misconceptions the author says surfaced while relying on AI across the full cycle. The first, reflected in the title, is the belief that supplying a specification or a set of design rules is enough to guarantee consistent, high-quality output. In practice, the author reports, a model can accept a rules document, appear to follow it, and still produce code that looks plausible on the surface while quietly diverging from intent or skipping the harder parts of a problem. The second thread concerns the reality on the ground: how the model behaves when a task is ambiguous, and how easily "it compiles and reads cleanly" can be mistaken for "it is correct." The stated goal is prompt design that prevents the AI from escaping into vague or superficial implementations—what the author frames as stopping it from getting by with "plausible-looking code."

Some context on the tooling helps explain why rules alone may fall short. Cursor is an AI-first code editor built on a fork of Visual Studio Code, and it supports a mechanism for encoding project conventions in rules files that are supposed to steer the model's behavior. But large language models are probabilistic text generators, not rule interpreters, and a rules file competes for the model's limited attention alongside the immediate prompt, the open files, and the conversation history. A convention that is written down is not necessarily one the model weights heavily at the moment it generates a function. That gap appears to be the practical source of the "I gave it the rules, so it should be fine" disappointment the author describes.

The remedy the author advocates is prompt design that closes the gaps rather than assuming a spec will be honored automatically. Approaches consistent with this account include forcing the model to state its assumptions before writing code, asking it to explain its reasoning so shallow choices become visible, and tying requirements to verifiable tests instead of prose descriptions. This is where the testing angle of the workflow becomes important: when implementation and test code are both AI-generated, tests can serve as an executable check on whether the code actually satisfies intent, though they only help if the tests themselves are meaningful rather than tautological. Techniques such as test-driven prompting, where expected behavior is specified first, are a natural fit for catching output that reads well but does the wrong thing.

The experience also sits within a broader industry moment. Agentic coding tools—Cursor's own agent mode, alongside offerings like GitHub Copilot, Claude Code, Windsurf, and Aider—increasingly run commands, edit multiple files, and execute tests on their own, which raises the stakes for how instructions are phrased. Many teams have adopted rules or configuration files as a way to standardize AI behavior across a codebase, and the author's account is a useful counterweight to the idea that such files are a complete solution. It is likely that as more organizations push AI further into design and testing, the discipline of prompt engineering will be treated less as a trick and more as a maintained part of the development process.

For readers evaluating similar workflows, the takeaway is measured rather than cautionary: AI can plausibly drive design conventions, implementation, and test creation, but the human role shifts toward specifying intent precisely, demanding transparency about assumptions, and verifying output through tests instead of trusting appearances. The article reads as field-tested advice from someone doing exactly that, and its value lies in naming the failure modes that a polished-looking result can conceal.