Originally published on Naveed Ahmed's Engineering Blog.
AI coding assistants have changed how quickly developers can build applications.
Describe a feature, generate code, run it, and ask the AI to fix whatever doesn't work.
For a small prototype, that's often a great workflow.
But what happens when the project grows, requirements change, and several engineers need to maintain the same codebase?
That's where the difference between vibe coding and Spec-Driven Development becomes important.
1. What Is Vibe Coding?
Vibe coding typically follows a conversational workflow:
Prompt
↓
Generate Code
↓
Run Application
↓
Fix With Another Prompt
↓
Repeat
It can be useful for prototypes, experiments, and quickly testing ideas.
The challenge appears when important requirements remain unstated.
An AI assistant might choose an unexpected architecture, overlook edge cases, or modify unrelated code during later iterations.
These aren't inevitable outcomes, but they're risks worth managing.
2. What Is Spec-Driven Development?
Spec-Driven Development (SDD) puts requirements and architecture ahead of code generation.
Instead of starting with implementation, you establish a specification that developers and AI agents can use as a shared reference.
A typical workflow looks like this:
Requirements
↓
Technical Design
↓
Implementation Tasks
↓
Code Generation
↓
Tests and Review
A good specification includes:
- Functional requirements
- Acceptance criteria
- API and data contracts
- Security constraints
- Architecture decisions
- Testing expectations
The goal isn't to write more documentation for its own sake.
It's to make important decisions explicit before implementation begins.
3. GitHub Spec Kit
GitHub Spec Kit is an open-source toolkit for organizing specification-driven workflows with AI coding assistants.
It provides a CLI and reusable templates for project rules, feature requirements, technical planning, and implementation tasks.
You can install the Specify CLI using uv:
uv tool install specify-cli \
--from git+https://github.com/github/spec-kit.git
Initialize a project:
specify init auth-service --ai claude
Inside your coding-agent workflow, you can then define requirements, create a plan, and break implementation into tasks.
The exact supported commands depend on the installed Spec Kit version and agent integration.
The important part is maintaining reviewable artifacts rather than depending entirely on chat history.
4. Why This Matters for DevOps
The same problem appears when AI generates Kubernetes manifests or Terraform configurations.
For example, asking:
"Deploy my API to Kubernetes"
doesn't specify:
- CPU and memory requests
- Security context
- Readiness probes
- Network access
- Secrets management
- Observability
- Rollback requirements
A structured infrastructure specification makes these constraints visible before code is generated.
You might define:
deployment:
replicas: 3
resourceRequests:
cpu: 250m
memory: 512Mi
security:
runAsNonRoot: true
allowPrivilegeEscalation: false
readinessProbe:
required: true
This is an illustrative specification, not a Kubernetes Deployment manifest.
Your tooling can use these requirements to generate and validate actual Kubernetes resources.
5. When Should You Use Each Approach?
| Situation | Suggested approach |
|---|---|
| Quick prototype | Vibe coding |
| Exploring an idea | Vibe coding |
| Production API | Spec-driven workflow |
| Multi-developer project | Spec-driven workflow |
| Security-sensitive feature | Specification and review |
| Kubernetes infrastructure | Specification, policy checks, and tests |
You can also combine the two approaches.
Prototype quickly, discover the requirements, then formalize them before building a maintainable production system.
Read the Complete GitHub Spec Kit Guide
My full article explores:
- The limitations of unstructured AI coding
- Vibe coding vs Spec-Driven Development
- GitHub Spec Kit architecture
- Installing and using Specify CLI
- Project constitutions and technical plans
- AI-assisted Kubernetes and Terraform workflows
- Production validation and engineering guardrails
Read the Complete Vibe Coding vs Spec-Driven Development Guide
Final Thoughts
AI can help teams write code faster.
But speed doesn't replace requirements, architecture, security, or verification.
For prototypes, a conversational workflow may be enough.
For systems people depend on, specifications make collaboration and review easier.
Are you using vibe coding, GitHub Spec Kit, or another structured AI coding workflow?
Share your experience in the comments.
Written by Naveed Ahmed — Lead DevOps & Platform Architect
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