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: AI-Driven Development
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…
The New Bottleneck: Why Clarity Matters More Than Code
For two decades, the fastest engineers were the ones who could write code quickly. They knew the shortcuts, the patterns, the frameworks. Their fingers moved faster than their competitors. That era is ending. The new bottleneck isn’t your typing speed or your syntax recall. It’s your clarity. I’ve spent twenty years building software, leading teams,…
Evaluate: Why Human Judgment Is Non-Negotiable
We have arrived at the phase of ADD where the most important human skill comes into play. You have written a specification. You have generated code using appropriate context and patterns. Now you must determine whether that code is actually correct. This is not a formality. AI-generated code can be syntactically correct, pass basic tests,…
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…
Prompt Patterns Catalog: Decomposition, Exemplar, Constraint
Software developers are familiar with design patterns. The Gang of Four cataloged reusable solutions to recurring problems in object-oriented design. You learn patterns like Strategy, Observer, and Factory not because they are theoretically interesting but because they solve problems you encounter repeatedly. Once you know the pattern, you recognize the problem and reach for 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…
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 a bug. But writing detailed specifications from scratch is cognitively demanding. You must simultaneously consider functional requirements, constraints, context, edge cases, and integration…
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…