AI Prototype as an Executable Requirements Draft

10× Faster Development with AI prototypes.

A prototype generated by a non-technical stakeholder can be very useful, but I do not treat it as an unfinished production system.

I treat it as an executable requirements draft.

The prototype gives stakeholders and domain experts something concrete to think of, build, analyze and react to, which makes product ideation and requirements elicitation much faster. Instead of discussing an abstract idea, they can directly build their ideas and visions into a prototype, actually designing the product without distracting business analytics or engineers.

The prototype code itself is a throwaway. It is usually written in a non-modular, code-and-fix style; it may lack meaningful error handling, fail to cover the use cases fully, or contain security flaws. Trying to clean it up directly can take longer than building the product correctly from the extracted requirements. But it already contains roughly formalized live product intentions, expressed by and aligned with non-technical stakeholders.

When the prototype is made, the actual engineering starts. I analyze the prototype with an AI agent and extract actors, use cases, business rules, user stories, scenarios, failure modes and edge cases. Then I model the domain context, components, interfaces and core workflows, align the result with stakeholders, and only after that design and implement the real system.

The speed gain comes from shortened ideation, stakeholder alignment and requirements formalization phases. That is the AI prototype part. It also comes from using AI across the whole engineering process, not only for code generation. It helps analyze the prototype, maintain C4 and UML models, update OpenAPI contracts, explore alternatives, generate tests and code, and keep related artifacts aligned when the design changes.

As one recent example, I designed a queue and transactional outbox subsystem, then aligned and refactored the surrounding architectural and behavioral models and aligned them with the contract and code in about four to five hours. Without AI assistance, that alignment work alone would take me a day or two.

Based on my practical experience, three to ten times faster is a reasonable ballpark for the entire stack of tasks, depending on the system and how much uncertainty exists at the beginning. It is not a universal benchmark, but it is also not only faster typing.

Engineering is freed from a big portion of requirements elicitation, prototyping and alignment and is granted more speed in analysis, modeling and implementation. I find that I enjoy development noticeably more than before AI. It is worth mentioning that I have greatly enjoyed it all the time.