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Spec Driven Development with GitHub’s Spec Kit vs Vibe Coding
If you have been building software with AI coding assistants over the last year, you have almost certainly felt the tension between speed and control. On one side, there is the intoxicating productivity boost of typing a prompt, watching an LLM generate hundreds of lines of code, and shipping something that appears to work. On the other side, there is the creeping realization that the codebase no longer clearly reflects your intent, architectural decisions feel accidental, and future changes are harder than they should be. This tension is at the heart of the debate between what has become known as vibe coding and a more disciplined approach called Spec Driven Development, especially as implemented by GitHub’s Spec Kit.
In this article, we are going to take a deep, technical, and practical look at Spec Driven Development with GitHub’s Spec Kit and compare it directly to vibe coding. We will explore how each approach works, why they feel so different in day-to-day development, and what trade-offs they introduce for individuals, startups, and large DevSecOps teams. By the end, you should have a clear mental model for when vibe coding is acceptable, when it becomes dangerous, and why Spec Driven Development is emerging as a necessary evolution in AI-assisted software engineering.
Understanding Vibe Coding: The Allure of Pure AI Improvisation 🔗
Vibe coding is not a formal methodology; it is a behavior pattern that emerged organically as developers started working with powerful large language models. The core idea is simple: you describe what you want in natural language, let the AI generate code, and then iteratively tweak prompts or code until the result “feels right.” There is no upfront specification, no formal plan, and often no explicit documentation beyond the prompts themselves.
This approach feels incredibly productive, especially for greenfield projects or exploratory work. You can spin up a REST API, a frontend component, or a data processing pipeline in minutes. The feedback loop is tight: prompt, generate, run, adjust. For solo developers or rapid prototypes, vibe coding can feel like having a senior engineer pair-programming with you at all times.
The problem is not that vibe coding produces bad code by default. In fact, modern AI models often generate code that is syntactically correct, idiomatic, and even reasonably well-structured. The real issue lies deeper: the intent behind the code is implicit, fragmented across prompts, and rarely preserved in a durable form. Architectural decisions emerge accidentally. Security assumptions are rarely explicit. Edge cases are handled inconsistently.
From a maintenance perspective, vibe-coded systems tend to accumulate invisible debt. When something breaks months later, the team has no clear artifact explaining why a particular approach was chosen. When compliance or security reviews happen, there is no audit trail. When a new developer joins, they inherit code without a narrative.
In short, vibe coding optimizes for immediate velocity at the expense of long-term clarity. That trade-off is not always wrong, but it is rarely made consciously.
What Is Spec Driven Development and Why It Matters Now 🔗
Spec Driven Development (SDD) flips the vibe coding model on its head. Instead of starting with code and hoping understanding emerges later, SDD starts with a specification that captures the what and the why before any implementation details are introduced. Code becomes a downstream artifact of a clearly articulated intent.
This idea is not new. Traditional software engineering has long emphasized requirements documents, design specs, and architecture reviews. What is new is how SDD integrates directly with AI coding agents. Rather than replacing specs, AI makes them more valuable by turning them into executable guidance.
GitHub’s Spec Kit is a concrete implementation of this philosophy. According to GitHub and Microsoft’s developer advocacy teams, Spec Kit provides a structured workflow that helps teams collaborate with AI in a predictable and auditable way. The workflow emphasizes four phases: Specify, Plan, Tasks, and Implement. Each phase produces artifacts that feed into the next, creating a continuous feedback loop instead of a linear handoff.
Why does this matter now? Because AI-assisted development has lowered the cost of writing code dramatically, but it has not lowered the cost of understanding systems. In many cases, it has increased it. Spec Driven Development recognizes that understanding, alignment, and traceability are now the scarce resources, not keystrokes.
For teams operating in regulated environments, or simply building software meant to last, SDD offers a way to reclaim control without giving up the productivity benefits of AI. It reframes AI as an amplifier of good engineering practices rather than a replacement for them.
Inside GitHub Spec Kit: A Structured Workflow for AI Collaboration 🔗
GitHub Spec Kit is an open-source toolkit designed to operationalize Spec Driven Development in AI-assisted workflows. Rather than being a single tool, it is a collection of templates, conventions, and a CLI that integrates with popular AI coding agents such as GitHub Copilot and others.
The heart of Spec Kit is its four-phase workflow:
- Specify: Define user outcomes, goals, constraints, and success criteria in plain language.
- Plan: Translate the specification into an explicit architecture, identifying components, technologies, and trade-offs.
- Tasks: Break the plan into actionable work items with acceptance criteria.
- Implement: Generate and refine code that is validated against both the specification and the plan.
What makes this workflow powerful is not just the sequence, but the feedback loops between phases. If implementation reveals a flaw in the plan, the plan is updated. If the plan exposes ambiguity in the spec, the spec is refined. The specification remains the single source of truth.
GitHub positions Spec Kit as a way to make AI output more predictable and reviewable. Instead of reviewing thousands of lines of generated code, reviewers can evaluate whether the implementation satisfies an agreed-upon specification. This dramatically changes the cognitive load of code reviews.
You can explore an example repository that contrasts Spec Kit workflows with vibe coding experiments in the public GitHub project at github/spec-kit. The repository demonstrates how structure changes both the process and the outcome of AI-assisted development.
The Specify Phase: Capturing Intent Before Code Exists 🔗
The Specify phase is arguably the most important and the most misunderstood part of Spec Driven Development. This is where teams articulate what they are building and why, without prematurely committing to technical details.
A good specification answers questions like:
- Who is the user, and what problem are we solving for them?
- What does success look like, and how will we measure it?
- What constraints exist (performance, security, compliance, budget)?
- What is explicitly out of scope?
In a vibe coding workflow, these answers often live in a developer’s head or are scattered across chat prompts. In Spec Kit, they are captured in a durable artifact that can be read by humans and AI alike.
This phase also sets the tone for collaboration. When an AI agent is given a clear specification, it can generate code that aligns with user outcomes instead of guessing based on patterns. This reduces the number of “almost right” solutions that need to be reworked later.
Importantly, the Specify phase is not about perfection. It is about shared understanding. Specs can evolve, but changes are explicit and traceable. This alone can eliminate a surprising amount of friction in AI-assisted projects.
Planning and Architecture: Where Spec Kit Pulls Ahead 🔗
The Plan phase is where Spec Kit creates the biggest contrast with vibe coding. Instead of letting architecture emerge implicitly from generated code, Spec Driven Development forces architecture to be explicit and reviewable.
In this phase, teams decide on things like service boundaries, data models, integration points, and non-functional requirements. These decisions are documented in a way that AI agents can reference during implementation.
Why does this matter? Because AI models are excellent at local optimization but weak at global architectural consistency. Without guidance, they may introduce unnecessary abstractions, inconsistent patterns, or hidden dependencies. A clear plan constrains the solution space in a productive way.
From a DevSecOps perspective, this phase is also where security and compliance concerns are surfaced early. Authentication strategies, data handling rules, and deployment constraints can be specified before code exists, reducing the risk of expensive rework.
GitHub’s documentation emphasizes that the Plan phase is not meant to be heavyweight or bureaucratic. It is a living document that evolves alongside the code, supported by AI rather than replaced by it. More details on this approach can be found in the official Microsoft developer blog post Diving Into Spec-Driven Development With GitHub Spec Kit.
Tasks and Implementation: Turning Specs into Executable Reality 🔗
Once a specification and plan are in place, Spec Kit moves into the Tasks and Implement phases. This is where AI-assisted coding shines, but within clear boundaries.
Tasks are derived directly from the plan and include acceptance criteria. This creates a natural contract between human intent and AI execution. Instead of asking the AI to “build a user authentication system,” you ask it to implement a specific task with defined inputs, outputs, and constraints.
Here is a simplified example of how a task might guide AI-generated code:
/** Task: Implement user login endpoint Acceptance Criteria: - Accepts email and password - Validates credentials securely - Returns JWT on success - Logs failed attempts */
The AI is no longer guessing. It is executing against a contract. Reviewers can then validate the implementation against the acceptance criteria rather than subjective notions of code quality.
This approach also makes refactoring safer. If requirements change, tasks are updated, and the AI can re-generate or modify code with awareness of the new constraints. The spec remains the anchor point.
Auditability, Security, and Compliance: The Hidden Cost of Vibe Coding 🔗
One of the strongest arguments for Spec Driven Development comes from security and compliance. In regulated industries, undocumented decisions are liabilities. Vibe coding, by its nature, produces minimal audit trails.
Spec Kit addresses this by making intent explicit and persistent. Specifications, plans, and tasks form a narrative that explains not just what the system does, but why it does it that way. This is invaluable during audits, incident response, and security reviews.
For example, when a security control is questioned, teams can point to the specification that required it, the plan that implemented it, and the code that enforces it. This traceability is nearly impossible to reconstruct after the fact in a vibe-coded system.
As noted in community discussions and articles like GitHub Spec-Kit: Why Structured AI Development Beats Vibe Coding, this structured approach aligns naturally with DevSecOps practices, where automation and documentation go hand in hand.
Team Collaboration and Knowledge Transfer 🔗
Another critical difference between Spec Driven Development and vibe coding is how knowledge is shared across a team. Vibe coding often creates knowledge silos, where understanding lives in prompt histories or individual minds.
Spec Kit turns knowledge into artifacts. New team members can read the specification and plan to understand the system’s intent before touching code. This reduces onboarding time and lowers the risk of accidental regressions.
For distributed teams, this clarity is even more valuable. Asynchronous collaboration becomes easier when decisions are documented and discoverable. AI agents become consistent collaborators rather than unpredictable contributors.
When Vibe Coding Still Makes Sense 🔗
Despite its drawbacks, vibe coding is not inherently bad. It excels in exploratory phases, prototypes, and learning environments. When the goal is to discover what you want, over-specifying too early can be counterproductive.
The key is intentionality. Use vibe coding to explore, then transition to Spec Driven Development once the problem space stabilizes. Treat early AI-generated code as disposable, not foundational.
Spec Kit does not eliminate creativity; it channels it. By separating exploration from execution, teams can enjoy the best of both worlds.
Adopting Spec Driven Development in Practice 🔗
Transitioning from vibe coding to Spec Driven Development does not require a big-bang change. Teams can start small by introducing lightweight specs for new features or critical components.
Practical steps include:
- Writing a one-page specification before major AI-assisted changes
- Reviewing specs as part of pull request workflows
- Using tasks with acceptance criteria to guide AI generation
Over time, these practices compound, leading to more predictable outcomes and higher confidence in AI-generated code.
The Future of AI-Assisted Development: Structure as a Force Multiplier 🔗
As AI models continue to improve, the temptation to rely solely on vibe coding will grow. Ironically, this makes structure more important, not less. The faster we can generate code, the more we need mechanisms to ensure it aligns with human intent.
Spec Driven Development, and tools like GitHub Spec Kit, represent an early but important step in this direction. They acknowledge that AI is a powerful collaborator, but one that needs context, constraints, and clarity to deliver sustainable value.
In an era where code is cheap, understanding is expensive. Spec Driven Development invests where it matters most.
Conclusion: Choosing Discipline Without Sacrificing Speed 🔗
The choice between Spec Driven Development and vibe coding is not binary. It is a spectrum. What GitHub’s Spec Kit offers is a way to move along that spectrum intentionally, trading a small amount of upfront effort for a large increase in long-term clarity and control.
If you are building software meant to last, especially with AI in the loop, Spec Driven Development is not overhead. It is leverage.
References 🔗
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