In recent years, AI-powered coding has become mainstream. Tools like GitHub Copilot, Claude Code, and Gemini CLI have made code generation faster and more accessible. But if you’ve ever tried “vibe coding”—simply prompting an AI to build a feature—you probably noticed the results often look correct but don’t fully work.
The issue isn’t always with the AI model—it’s usually with unclear specifications. That’s where spec-driven development (SDD) comes in. GitHub has now introduced an open-source toolkit called SpecKit, designed to bring structure, clarity, and precision to AI-assisted software development.
What is Spec-Driven Development?
Traditional development starts with code, followed by documentation. Spec-driven development flips this around—you begin by writing a living, executable specification that defines what you want to build and why.
This becomes the source of truth for all stakeholders and AI coding agents. Instead of vague prompts, AI now works against a structured specification, leading to cleaner, safer, and more reliable code.
AI excels at patterns, but it can’t read your mind. Prompts like “Add photo sharing to my app” leave too many assumptions. Spec-driven development eliminates the guesswork.
Introducing GitHub SpecKit
GitHub’s SpecKit is an open-source toolkit that formalizes the SDD workflow. It includes:
- A CLI tool
- Specification templates
- Steering prompts
- Integration with AI agents like GitHub Copilot, Claude Code, and Gemini CLI
Instead of ad-hoc prompting, SpecKit organizes development into four gated phases, each with validation checkpoints.
The Four Phases of SpecKit
- Specify
- Define what you want to build and why.
- Focus on user journeys and expected outcomes.
- AI generates a detailed evolving spec with user stories, acceptance scenarios, edge cases, and functional requirements.
- Plan
- Choose the tech stack and set architectural constraints.
- AI generates technical plans, data models, contracts, and research documents.
- Ensures reasoning is transparent, aligning design decisions with developer intent.
- Tasks
- Break the project into small, testable tasks.
- AI creates a numbered task list for clear, organized execution.
- Implement
- AI executes tasks incrementally.
- Developers review and approve each change before moving forward.
- Prevents bulky, error-prone code dumps.
Demo: Building a Pokedex Team Builder
In the video demo, SpecKit was used to build a Pokedex Team Builder app:
- Specify phase: Generated user stories and acceptance criteria.
- Plan phase: Created a Zod schema for data modeling and reasoning docs explaining stack decisions.
- Tasks phase: Produced an organized step-by-step task list.
- Implement phase: AI executed setup and feature tasks incrementally, producing clean code and a functional app.
The final result was a polished project where the API worked seamlessly, the UI was clean, and the process felt controlled rather than chaotic.
Why Spec-Driven Development Matters
- Clarity: Everyone (humans + AI) works off the same specification.
- Reliability: Prevents half-baked features and code dumps.
- Control: Developers can refine direction at every checkpoint.
- Future-Proofing: Sets the foundation for AI coding agents to act more like disciplined teammates than search engines.
That said, the choice of coding model still matters. For example, GPT-4.1 underperformed in this demo compared to the Grok model. SpecKit works best when paired with capable AI models.
Final Thoughts
SpecKit represents a big step forward in how AI coding workflows are structured. By turning intent into executable specifications, it bridges the gap between what developers want and what AI delivers.
As AI coding agents evolve, spec-driven development will likely become a mainstream paradigm for software teams.
Are you experimenting with SpecKit or other spec-driven development frameworks like Amazon’s Kira? Share your experience in the comments!
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