Why a specification-first approach may shape the next generation of AI-assisted development
In the rush to bring AI into modern software development, many teams have started where it feels natural—by letting AI write the code. But as organizations scale their use of AI Coding Agents, they’re discovering a deeper challenge: governance.
How do you ensure consistent standards, context, and quality when large language models are making thousands of micro-decisions on your behalf?
That’s where Spec-Driven Development (SDD) enters the conversation.
At its core, SDD turns the traditional workflow upside down: instead of starting with code and documenting later, you start with an executable specification that becomes the single source of truth for how features are designed, built, and validated.
It’s an emerging model for what’s being called AI-first development—where human intent and AI automation work hand-in-hand through clearly defined, enforceable context.
What Is Spec-Driven Development?
Spec-Driven Development (SDD) replaces ad-hoc prompt engineering with formal, version-controlled specifications that guide AI Coding Agents.
Rather than feeding an AI a paragraph of natural-language instructions and hoping for consistency, SDD defines a structured workflow around an executable spec that includes:
- A clear definition of behavior — describing what a feature must do, not how it should be built.
- A governing “Constitution” — a set of non-negotiable rules and standards that shape every output.
- Automated enforcement — specifications that can be validated, tested, and refined through interaction with AI Coding Agents.
The goal is simple: eliminate ambiguity. By embedding governance directly into the AI development process, teams can reduce drift, improve quality, and capture institutional knowledge in a durable, reusable format.
Why It Matters
Most engineering teams face the same growing pains with AI enablement:
- Context fragmentation. Critical architectural decisions are buried across Slack, Jira, and wikis.
- Inconsistent outputs. Different developers—or different AIs—solve the same problem in different ways.
- Scaling governance. Manual code reviews and informal standards don’t scale when machines write code.
SDD directly addresses these problems by making the specification the authoritative artifact that ties together requirements, governance, and implementation.
As nvisia Principal Architect Michael Hoffman puts it in his new white paper:
“A prompt is just an instruction—it lacks the formal, machine-readable structure required to enforce standards. The specification becomes the ultimate shared context.”
Experimenting with GitHub’s Spec Kit
To explore how SDD might work in practice, Hoffman built a proof-of-concept fraud detection platform using the open-source GitHub Spec Kit.
The Spec Kit provides a structured, four-phase workflow:
- Specification – define the business and functional intent.
- Planning – translate that intent into architecture and design.
- Tasking – create atomic, executable steps for the AI.
- Implementation – guide and verify the AI’s output.
This workflow is enforced by a “Constitution” file—essentially a project’s governance document that codifies standards like security, performance, and technology preferences.
In Hoffman’s experiment, the AI Coding Agent generated not only the initial specifications and plans but also corresponding tasks and code implementations, all under human supervision.
He tested different configurations across three GitHub repositories—front-end, back-end, and master specification—and even designed a knowledge-base hierarchy to automatically generate Constitution files for each project component.
Early Findings: Promise and Pain Points
The experiment validated SDD’s potential—but also surfaced the practical hurdles that any team exploring it will face.
✅ Centralized knowledge reduces friction.
Having a specification serve as a living source of truth made onboarding faster and helped ensure consistent design intent across multiple repositories.
✅ Contextual governance improves quality.
Embedding compliance and performance standards in the Constitution file gave AI Coding Agents more reliable guidance during generation.
⚠️ Over-specification creates bottlenecks.
AI agents can become overwhelmed by excessively detailed specs, requiring careful scope management and iteration.
⚠️ Workflow rigidity limits flexibility.
Late-stage architectural changes often required revisiting multiple earlier phases—something that can slow down smaller, more agile teams.
⚠️ Early-stage enablement takes time.
Configuring repositories, constitutions, and context structures adds up-front effort that may deter teams expecting immediate gains.
Even so, the lessons are instructive. SDD shows promise for rapid prototyping, greenfield development, and highly regulated environments where traceability and compliance are essential.
As Hoffman notes, “The best way to understand this new workflow is to see it for yourself.”
A Glimpse Into the Future of AI-Assisted Development
Spec-Driven Development is not a silver bullet—yet. But it represents an important evolution in how engineering teams think about AI governance, intent, and context.
By shifting focus from “writing code” to “defining specifications,” organizations can better align human expertise with machine efficiency.
In the same way agile replaced waterfall and DevOps replaced handoffs, SDD could become the next cultural transformation in software delivery—one that treats context as code and governance as automation.
At nvisia, we see these experiments as more than academic. They reflect a practical exploration of AI-first development—how to integrate new tools responsibly without sacrificing quality, architecture, or control.
Explore the Full White Paper
Michael’s full write-up, “Spec-Driven Development: My Journey Using the GitHub Spec Kit,” is available as a downloadable white paper.
Dive deeper into his implementation walkthrough, technical artifacts, and detailed lessons learned:
➡️ Download the full white paper (PDF)
About the Author
Michael Hoffman, Technical Director – nvisia
Michael Hoffman is a Principal Architect at nvisia and a Pluralsight author with over 25 years of experience designing enterprise solutions across diverse industries. A lifelong technologist and mentor, he’s passionate about bridging the gap between engineering and product development.
Michael’s technical expertise spans front-end and back-end architecture, with a focus on service-based APIs and Java solutions leveraging the Spring Framework. He has implemented large-scale features on IBM platforms, including WebSphere Commerce and WebSphere Portal, and regularly works with frameworks such as AngularJS, jQuery, Apache Camel, and Java.
At nvisia, Michael helps enterprise teams navigate the transition to AI-assisted development—combining deep architectural rigor with a forward-looking approach to modern engineering practices.
Connect with Michael on LinkedIn or explore his Pluralsight courses.