Code that passes evaluation is ready for integration. This is the final phase of the ADD cycle, where generated code becomes part of your system. But integration is more than merging a pull request. It is where AI-generated code meets the full reality of your codebase, your testing infrastructure, your deployment pipeline, and your team’s…
Category: Software Engineering
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, even experienced developers skip evaluation steps. They focus on the dimensions they find most interesting or most familiar, and they neglect the others….
You Don’t Want a Claude Code Guru
The job posting practically writes itself these days. “Looking for a senior developer proficient with AI coding tools. Must be comfortable using Claude Code, Cursor, or Copilot to rapidly produce production-ready code. We need someone who can 10x our output.” I have seen variations of this everywhere over the past year. Companies scrambling to find…
Prompt Patterns Catalog, Part 2: Iteration, Verification, and Persona
In the previous post, I introduced three foundational prompt patterns: Decomposition for breaking complex tasks into manageable units, Exemplar for teaching by example, and Constraint for defining boundaries. These patterns address the most common generation challenges. This post completes the catalog with three more patterns, then addresses the practical question of building and maintaining a…
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 most developers start, jumping directly to generation without the specification work that should precede it. In the ADD cycle, generation is the second…
Specify: The Most Important Skill in AI-Driven Development
If you take one thing from this entire series, let it be this: the quality of AI-generated code is bounded by the quality of your specification. No amount of model capability, prompt engineering tricks, or iteration can overcome a vague specification. The ceiling of what AI can produce for you is set by the clarity…
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 problems. Understanding this history matters because AI-assisted development is at an inflection point. The unstructured approaches I described in my previous post are…
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 shift I have witnessed in two decades of software engineering. But here is the uncomfortable truth: most of us are using these tools…
ADD: AI-Driven Development as a Methodology for the Future Engineer
Software development has always evolved through methodologies that structure how we think about building systems. Waterfall gave way to Agile. Test-Driven Development changed how we approach correctness. Behavior-Driven Development shifted focus toward specifications that non-technical stakeholders could understand. Each methodology emerged because the existing approaches no longer fit the reality of how software was actually…