From Prototype to Production: Vibe Coding as Prototype

So you fired up your AI agent of choice and gave it a simple demand: “build me a thing.” The agent diligently toils away, perhaps asking questions, perhaps just diving in. But, before you know it, you have “a thing.”

What should you do with this thing?

Something has been built, so the hair-trigger instinct might be to say, "Ship it!" But should you? Is it in what developers might call a “shippable” state? What are the ramifications of launching this thing?

The truth is, unless you prompted very deliberately — knowing quite a bit about how you planned to deploy your project, host it, maintain it, secure it, and a whole lot more — your app might not be quite ready for production. Let's talk about what happens next.

First, make the deferred decisions.

Before we go any further, know that the process of asking an AI agent to build something is a discovery project. It is a prototype, and it is designed not to be perfect as is. In fact, when quickly vibe-coding up a simple app or prototype, I rarely pay attention to concerns that don’t immediately affect the result, such as:

• The source code language and frameworks used
• Deployment and hosting requirements
• Accessibility and security concerns
• Long-term maintenance
• Testability and verification

But these are vital for production-ready code, which means your prototype needs a bit more definition before it's ready to be deployed. Now is the time to make the calls and put them in writing.

An agent can helpfully make suggestions, but the best answers will always be the ones that suit your specific needs and situation, which requires expertise beyond the purely technical. And in most of these cases, the decision comes down to a set of tradeoffs for what will be the most favorable to your goals and capabilities.

Nailing this down before any code gets written gives your team and AI agents the guidance and context necessary to build a production-ready solution.

Prototype as specification.

The prototype may include a significant amount of code, but now its primary value is as a specification. True, the prototype most likely already contains the features and behavior you were after, but it wasn’t written with all the context and decisions necessary to build a cohesive and elegant solution for your situation. But we can treat the prototype as a specification to guide us to that solution.

This starts with an exploration of the prototype, followed by a deep extraction of how the prototype behaves, meets user expectations, and serves the goals of the business. This is an interactive process as we use agents to dig through the prototype, documenting and confirming behaviors and decisions that were often made on the user’s behalf, and combining this documentation with the decisions we made earlier. All of this coalesces into a highly focused and tuned technical specification.

Building from the spec.

Once the specification is fully defined, we don’t necessarily throw the prototype away. The prototype can continue to be a resource to compare against as the real build comes together.

The build cycle itself goes something like this:

• We break the specification down into distinct steps and plan each step separately
• The plan for each step includes what’s being built, guidance on how it will be built, and tests and rules for verification
• Each step is implemented, including automated testing and self-verification fixtures
• Each step is code-reviewed and tweaked as necessary
• As appropriate, the in-progress and final builds may be compared against the prototype for completeness and accuracy
• The cycle continues until all steps are completed, followed by a final review and QA pass

Then the build is ready for client review. Of course, AI is heavily used throughout this process to build out pieces, write and execute tests, and more. Humans get involved on critical paths, key decisions, quality assurance, and agent guidance.

A prototype is a wonderful thing. I’ve often found that no matter how complete your vision may seem, you don’t necessarily know what you’re building until you’ve actually built it. The speed of AI gives us the ability to quickly build out a rough prototype version, allowing us to explore the problem space, the user experience, and possible hang-ups and solutions that may arise in a very rapid, iterative development cycle. An idea comes to life in hours, not weeks, and changes can be made rapidly. Even if the prototype isn’t launch-ready, it has served its purpose and provided enormous value. Let us help you take that prototype to the next level. Let’s ship it.