AI-Driven Development
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Evaluation Checklists: Building Your Quality Gate for AI Code
In the previous post, I covered the five dimensions of evaluating AI-generated code: correctness, fitness, security, performance, and maintainability. Understanding these dimensions is essential. But understanding is not enough. Under time pressure,…
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Generate: The Art of Effective AI Collaboration
Generation is where the visible work happens. You provide input, and the AI produces code. This is the moment most developers think of when they imagine AI-assisted development. It is also where…
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Specification Templates: A Practical Library for AI Development
In the previous post, I made the case that specification is the highest-leverage skill in AI-driven development. A precise specification produces better output, requires less iteration, and surfaces ambiguity before it becomes…
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From Waterfall to ADD: Why AI Demands Its Own Methodology
Software development methodologies do not emerge from academic theory or conference talks. They emerge from pain. Practitioners encounter problems that existing approaches cannot solve, and they develop new disciplines to address those…
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The Unstructured AI Problem: Why Most Teams Are Using AI Wrong
Every developer I know uses AI tools now. Copilot suggestions appear mid-keystroke. ChatGPT tabs stay permanently open. Claude conversations stretch across multiple projects. The adoption curve was vertical, faster than any technology…