What is Spec Driven Development

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

Spec Kit makes your specification the center of your engineering process. Instead of writing a spec and setting it aside, the spec drives the implementation, checklists, and task breakdowns.  Your primary role is to steer; the coding agent does the bulk of the writing.

It works in four phases with clear checkpoints. But here’s the key insight: each phase has a specific job, and you don’t move to the next one until the current task is fully validated. 

Here’s how the process breaks down:

Specify: You provide a high-level description of what you’re building and why, and the coding agent generates a detailed specification. This isn’t about technical stacks or app design. It’s about user journeys, experiences, and what success looks like. Who will use this? What problem does it solve for them? How will they interact with it? What outcomes matter? Think of it as mapping the user experience you want to create, and letting the coding agent flesh out the details. Crucially, this becomes a living artifact that evolves as you learn more about your users and their needs.

Plan: Now you get technical. In this phase, you provide the coding agent with your desired stack, architecture, and constraints, and the coding agent generates a comprehensive technical plan. If your company standardizes on certain technologies, this is where you say so. If you’re integrating with legacy systems, have compliance requirements, or have performance targets you need to hit … all of that goes here. You can also ask for multiple plan variations to compare and contrast different approaches. If you make your internal docs available to the coding agent, it can integrate your architectural patterns and standards directly into the plan. After all, a coding agent needs to understand the rules of the game before it starts playing.

Tasks: The coding agent takes the spec and the plan and breaks them down into actual work. It generates small, reviewable chunks that each solve a specific piece of the puzzle. Each task should be something you can implement and test in isolation; this is crucial because it gives the coding agent a way to validate its work and stay on track, almost like a test-driven development process for your AI agent. Instead of “build authentication,” you get concrete tasks like “create a user registration endpoint that validates email format.”

Implement: Your coding agent tackles the tasks one by one (or in parallel, where applicable). But here’s what’s different: instead of reviewing thousand-line code dumps, you, the developer, review focused changes that solve specific problems. The coding agent knows what it’s supposed to build because the specification told it. It knows how to build it because the plan told it. And it knows exactly what to work on because the task told it.

Crucially, your role isn’t just to steer. It’s to verify. At each phase, you reflect and refine. Does the spec capture what you actually want to build? Does the plan account for real-world constraints? Are there omissions or edge cases the AI missed? The process builds in explicit checkpoints for you to critique what’s been generated, spot gaps, and course correct before moving forward. The AI generates the artifacts; you ensure they’re right.