Vibe coding has transformed how we approach development. The ability to describe what you want and get working code immediately is genuinely powerful. Our teams are shipping prototypes faster than ever.

The challenge comes when those prototypes need to become production systems. Without clear requirements or documentation, maintenance becomes a nightmare and knowledge transfer fails.

This is where spec-driven development changes the game. Tools like kiro.dev take the same conversational AI approach but add the structure enterprise teams actually need. You still get the speed of AI-assisted coding, but with proper requirements, design documentation, and implementation plans.

Here’s what’s working for us: whether you’re using Claude Code with a claude.md file or AWS Q with custom rules, the key is defining your development standards upfront. Document your coding practices, architecture patterns, testing requirements, and security guidelines. Make these part of your AI interactions from the start.
For example, in our claude.md file, we specify: “All new features must follow test-driven development. Write tests first using the AAA pattern – Arrange (setup), Act (execute), Assert (verify). Include both positive and negative test cases. Use descriptive test names that explain the business scenario being tested.”

Instead of “build me a review system,” you get user stories, technical specifications, and task breakdowns that your entire team can follow. The AI understands your context and delivers code that fits your existing systems and standards, complete with comprehensive tests written before implementation.

This isn’t about replacing creativity with process. It’s about making AI a more reliable partner by being explicit about expectations. When your AI assistant knows your team’s definition of “done,” the results are significantly more predictable and production-ready.

The evolution from vibe coding to spec-driven development represents the maturation of AI-assisted development. We’re moving from impressive demos to sustainable systems.

What’s your experience with AI-assisted development? Are you seeing similar challenges moving from prototype to production?
Next week, I’ll be diving into why proper Git practices become even more critical when AI is writing code – and how poor version control can amplify technical debt exponentially.

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