We inherited a codebase last year that was 80% AI-generated.
The client had been vibe coding for six months. The app worked, barely. But nobody on the team could explain why any decision was made. No API contracts. No schemas. No audit trail. Just thousands of lines of code held together by chat history.
Rebuilding that took three times longer than building it right the first time would have. That’s not an AI problem. That’s an architecture problem. And today I’m going to show you the workflow that prevents it.
Here’s what’s actually happening on most teams using AI right now. The workflow goes:
Write a prompt → get code → something breaks → write another prompt → patch it → something else breaks.
You’re not developing. You’re prompt debugging. And you’re generating technical debt faster than any human team could have.
The root cause is that you’re asking the AI to guess the architecture. Without a defined contract, an LLM fills every gap with a statistical best guess. After six months, you don’t have a codebase; you have accumulated guesses.
Worse, the logic lives in your chat history, not your repository. The day you close that session, the reasoning is gone.
Now, spec-driven development isn’t new, but applying it as the entry point for AI is. The principle is simple: a spec is the deterministic boundary for a probabilistic model.
Read our blog, Advanced Prompt Engineering Strategies
Production Workflow of Spec-Driven Development: Step by Step
Step 1: Contract Definition
Write the spec first. Define your endpoints, your request/response schemas, your error codes, your auth model. If it’s a data pipeline, write the DB schema. If it’s a service layer, define the gRPC interface. If you can’t write it down as a typed contract, you don’t understand it well enough to build it yet.
Step 2: Spec Validation
Lint your spec. Run it through a validation tool: Spectral for OpenAPI, the protoc compiler for gRPC, Pydantic’s own validator. Make the contract solid on its own terms.
Skipping this is why the AI generates garbage, because it was handed an ambiguous contract and expected a precise result.
Step 3: Targeted Generation
Now you hand the spec to the LLM. With one explicit constraint in your prompt:
“Implement this interface. Do not change types. Do not invent fields. Do not add endpoints that aren’t defined here.”
This removes 80% of the hallucination surface area. The AI is implementing a contract you already validated. That’s a radically different cognitive task for the model, and the output quality reflects it
Now, having a better way to generate code is a great start, but it’s only half the battle.
In the next video, I’m going to show you the final step: how to turn this into a closed loop that validates itself, so you never have to babysit AI-generated PRs again.
Step 4: Automated Testing
This step is what makes this a closed loop, not just a better workflow.
Use the same spec to generate integration tests. Tools like Schemathesis for OpenAPI, or custom test harnesses driven by your Pydantic schemas.
Now your CI/CD pipeline has a ground truth to test against. If the AI-generated code fails a test against the spec, it is automatically discarded.
Think about what this means at scale. If your team is running 50 AI-assisted PRs a week, you’ve just replaced 50 manual code reviews with an automated spec-conformance check. Your engineers review the spec once. The system enforces it indefinitely.
Conclusion
In an AI-driven world, your IP isn’t the thousands of lines of generated code; any model can do that. Your IP is the specification. It’s your business rules, your data models, and your architectural decisions. The code is just a build artifact.
If a better model comes out tomorrow, teams with a spec can migrate their entire codebase in hours. Teams built on prompts are locked in, not by a vendor, but by the fact that no one documented the reasoning behind their software.
Beyond that, prompt-first is “cheap” today but “expensive” tomorrow. It shows up in your QA spend and engineering attrition.
Spec-driven development is an upfront investment that makes every downstream phase structurally faster. The teams that invest in specs today are the ones who can still move fast in two years.
Back to that client I mentioned at the start. When we went in to rescue the project, the first thing we did was reconstruction, rebuilding the API contracts that should have existed from day one. OpenAPI specs for every endpoint. Pydantic schemas for every data model.
Once those existed, we could hand them to an LLM and regenerate critical modules in a fraction of the time it took to build them the first time.
The spec made the rescue possible. Without it, we would have been rewriting by hand for months. The lesson is: you are the architect. The AI is the contractor. And no contractor can build what the architect hasn’t designed.
If your team is heading into a project with AI and you’re still starting from prompts, change the workflow now. The debt compounds, and when it does, the spec is the only thing that lets you dig out. Our AI engineers can help you!
