Instead of coding first and writing docs later, in spec-driven development, you start with a (you guessed it) spec. This is a contract for how your code should behave and becomes the source of truth your tools and AI agents use to generate, test, and validate code. The result is less guesswork, fewer surprises, and higher-quality code.

Reading

Articles/Blogs/Essays

• Spec-Driven Development with AI: "We treat coding agents like search engines when we should be treating them more like literal-minded pair programmers. They excel at pattern recognition but still need unambiguous instructions. That’s why we’re rethinking specifications — not as static documents, but as living, executable artifacts that evolve with the project. Specs become the shared source of truth. When something doesn’t make sense, you go back to the spec; when a project grows complex, you refine it; when tasks feel too large, you break them down."

  3 places this approach works really well:

  1. Greenfield (zero-to-one): When you’re starting a new project, it’s tempting to just start coding. But a small amount of upfront work to create a spec and a plan ensures the AI builds what you actually intend, not just a generic solution based on common patterns.
  2. Feature work in existing systems (N-to-N+1): This is where spec-driven development is most powerful. Adding features to a complex, existing codebase is hard. By creating a spec for the new feature, you force clarity on how it should interact with the existing system. The plan then encodes the architectural constraints, ensuring the new code feels native to the project instead of a bolted-on addition. This makes ongoing development faster and safer. To make this work, advanced context engineering practices might be needed — we’ll cover those separately.
  3. Legacy modernization: When you need to rebuild a legacy system, the original intent is often lost to time. With the spec-driven development process offered in Spec Kit, you can capture the essential business logic in a modern spec, design a fresh architecture in the plan, and then let the AI rebuild the system from the ground up, without carrying forward inherited technical debt.

The core benefit is separating the stable “what” from the flexible “how,” enabling iterative development without expensive rewrites. This allows you to build multiple versions and experiment quickly.

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Tags: language   spec-oriented   ai  

Last modified 11 October 2025

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