This repo documents my journey into spec-driven development, learning to write detailed specifications first and then letting AI agents handle the implementation.
Spec-driven development flips traditional coding on its head - instead of "vibe coding" where you describe your goal and hope the AI gets it right, you start with a clear specification that becomes the source of truth for your code. As Sean Grove from OpenAI puts it: "The person who communicates the best will be the most valuable programmer in the future."
Think of it like being a conductor with a clear musical score, rather than a jazz jam session where musicians might hit sour notes. Specifications become executable blueprints that AI agents convert directly into working implementations, eliminating the chaos of making up the design as you go.
Major tools like AWS Kiro, GitHub's SpecKit, and JetBrains are shifting from prompt-based "vibe coding" to spec-driven approaches because specifications produce more reliable, production-ready code. New developers are adopting this workflow because it brings structure and predictability to AI-assisted development.
- Learn Qwen Coder or Gemini CLI
- Integrate MCP servers for extended tooling
- Understand how agents interpret instructions
- Write clear, detailed specifications
- Create structured documentation
- Define requirements before code
- Feed specifications to coding agents
- Let agents generate implementation
- Review and iterate on results
- CLI Agents: Qwen Coder / Gemini CLI
- MCP Servers: Extended tooling and integrations
- Specification: SpecKit Plus
- Approach: Specification first, implementation second
Star this repo if you're exploring spec-driven development!