Modern software development is changing quickly.
Today, a large portion of application code is often generated with AI tools such as ChatGPT or GitHub Copilot.
But as AI coding becomes more common, an important question emerges:
What’s the best way to use AI when building large, complex applications?
One answer gaining traction is Spec Driven Development.
The Two Common AI Coding Approaches
Right now, most developers tend to fall into one of two approaches when building software with AI:
- Vibe Coding
- Spec Driven Development
Vibe Coding
Vibe coding is the most natural and widely used approach.
You prompt an AI model with a task, it generates code, and you continue refining the system through a series of prompts.
For example:
- “Create an API endpoint for user registration.”
- “Add validation logic.”
- “Generate a React component for displaying user profiles.”
For small features, prototypes, or experiments, vibe coding works extremely well. It’s fast, flexible, and often the easiest way to start building something.
But as a project grows, a few challenges start to appear.
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The Problem With Large AI-Generated Projects
Imagine building a large application such as a car classifieds platform with many business rules:
- Listing management
- Vehicle search filters
- User messaging
- Payment integration
- Moderation and approval workflows
This quickly becomes a large and complex system.
At that point, simply prompting an AI repeatedly can start to break down.
Common issues include:
- The AI loses project context between prompts
- It misinterprets requirements
- It generates code that works but slowly introduces technical debt
The root problem is simple:
The AI does not truly understand the full system context.
This is exactly the problem Spec Driven Development tries to solve.
What Is Spec Driven Development?
Spec Driven Development (SDD) is a structured workflow where AI generates code from formal specifications rather than individual prompts.
Instead of repeatedly asking the AI what to build next, the project is defined using structured documents that describe the system.
These specifications typically include:
- Application overview
- Technology stack
- Architecture guidelines
- Coding standards
- Features and user stories
- Tasks and acceptance criteria
The specifications are usually stored in Markdown files, allowing AI tools to reference them consistently throughout the project.
By providing this structured context, AI can generate code that fits the system architecture and long-term design.
How Spec Driven Development Works
Most Spec Driven frameworks follow a similar workflow.
1. Initialize the Project Context
The first step is defining the foundational specification for the project.
This includes things like:
- Application type
- Technology stack (for example .NET, SQL Server, React or Blazor)
- Libraries and dependencies
- Business rules
- Architectural constraints
- Acceptance criteria
For example, the GitHub Spec Kit framework calls this document the “constitution.”
This file acts as the core context for the AI agent.
2. Break the System Into Structured Artifacts
Instead of working from a giant requirement document, the framework breaks the system into smaller pieces such as:
- Epics
- Features
- User stories
- Implementation tasks
These artifacts give the AI a clear roadmap of what needs to be built.
3. Review Before Code Generation
Before the AI writes any code, developers review the generated specifications.
Once approved, the AI agent executes tasks based on the specification rather than guessing from a prompt.
This greatly reduces misunderstandings and incorrect implementations.
Popular Spec Driven Development Frameworks
Several frameworks have emerged to support this workflow, including:
- OpenSpec
- GitHub Spec Kit
- BMAD Method
- Kiro
- Tessl
These tools help manage specifications, organize requirements, and coordinate AI agents during development.
Spec Driven Development vs Vibe Coding
The key difference between the two approaches is how AI receives context.
Vibe Coding
- AI works mainly from prompts
- Context can easily be lost between sessions
- Works best for quick tasks and experimentation
Spec Driven Development
- AI works from structured specifications
- Maintains system context
- Better suited for large, complex applications
Vibe Coding Is Still Valuable
It’s important to note that vibe coding is not a bad approach.
In fact, it’s often the best way to start a project.
It’s ideal for:
- Rapid prototyping
- Small utilities
- Experiments
- Learning new frameworks
But once a project grows and starts to involve complex domain logic and long development cycles, a more structured workflow like Spec Driven Development becomes increasingly useful.
Final Thoughts
Spec Driven Development is essentially a way to turn AI into a structured member of the development team.
Instead of guessing the intent of each prompt, the AI works from a well-defined set of specifications that describe the system architecture and requirements.
As AI-assisted development becomes more common, workflows like this may become an important part of building larger, long-term software systems.
And in reality, most teams will likely use a combination of both approaches.
Sometimes you vibe code.
Sometimes you rely on structured specifications.
The key is knowing when each approach makes sense.
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