Spec-Driven Developers - Specs As Primary AI Interface

I’ve been exploring Spec-Driven Development (SDD) using GitHub’s Spec Kit, and it feels like a meaningful shift in how we can build greenfield software with AI. Most AI-assisted development today looks like this: Prompt → Code → Debug → More Prompts → More Code SDD introduces a different model: Intent → Specification → Clarification → Plan → Tasks → Implementation → Validation → Convergence The key idea is simple: the specification becomes the source of truth, and code becomes an implementation of that intent. A few takeaways stood out: * AI coding gets much stronger when ambiguity is removed before implementation. * Separating WHAT/WHY from HOW leads to better architecture decisions. * Engineering principles can be encoded as reusable guardrails through a project constitution. * Requirements, tasks, tests and implementation can become traceable rather than disconnected artifacts. * “Done” should mean the implementation matches the agreed intent, not simply that the code compiles. As AI gets better at generating code, I believe engineering value will increasingly move toward defining intent, challenging assumptions, making architectural trade-offs and governing how AI builds software. Working on a article explores the full Spec Kit workflow and a practical project I built to test it. Will share once it is ready. Would be interested to hear from others experimenting with Spec-Driven Development: Do you see specifications becoming the primary interface between humans and AI coding agents? #SpecDrivenDevelopment #SoftwareEngineering #GitHub #GenerativeAI #AgenticAI #SoftwareArchitecture #DeveloperExperience #AIEngineering