AI coding tools make it incredibly easy to build quick prototypes. Most developers are familiar with “vibe coding” — prompting an AI in chat and getting a working application almost instantly.
For example, you might ask an AI:
“Build a car classifieds website using .NET C# with Blazor.”
Within seconds you’ll usually get something that looks functional — maybe a simple UI for posting adverts and browsing listings.
For quick experiments and prototypes, this is amazing.
But once you try to build a larger application with real requirements, problems appear:
- Architecture is often messy
- Best practices are ignored
- Code becomes difficult to extend
- The AI-generated structure doesn’t scale well
This is where Spec-Driven Development becomes useful.
Instead of relying on a single high-level prompt, frameworks like OpenSpec, SpecKit, Kiro, and Bmad guide AI using structured specifications.
A large requirements document can be automatically broken down into:
- user stories
- implementation steps
- development tasks
This allows AI agents to generate code incrementally and more accurately, instead of improvising everything from a single prompt.
Another advantage is that spec-driven frameworks allow multiple AI agents to collaborate on the same project:
- one agent for data access
- one for business logic
- one for the front end
Instead of one AI doing everything, you effectively have an AI development team working from the same spec.
What This Series Shows
In this video series I demonstrate this approach by building a car classifieds web application using:
- .NET C#
- Blazor
- Visual Studio Code
OpenSpec for spec-driven workflows
The goal is to show how structured specifications can guide AI to generate cleaner, more maintainable code than typical vibe coding.
The first video starts by creating the basic solution structure.
If you’re exploring AI-assisted development beyond simple prompts, this series might give you some practical ideas. 🚀
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