Most teams do the first two steps. The third is the one that closes the loop.
The frontend workflow that makes the most of AI generation has three stages: design in Figma, generate an implementation using an AI coding tool, then verify the rendered output against the design before shipping. Most teams currently do the first two. The third is the step that closes the loop between what was designed and what actually gets built, and it is the step that AI generation made more necessary while simultaneously making it easier to skip.
In a traditional implementation cycle, a developer would spend a significant portion of their time on translation work: reading design specifications, copying colour values, calculating spacing, matching typography. This was tedious but it had a side effect. The act of working through the design manually meant the developer was looking at it closely, which created a natural opportunity to notice when something was not coming out right.
The implementation was slow enough that visual accuracy got checked incrementally — building and checking happened at the same pace.
AI tools collapsed the translation step. A developer using Cursor or a similar tool can go from design to implementation in minutes. The markup is there. The styles are close. The page roughly looks right.
What that speed removed is the incidental checking that slow implementation created. The page exists before the developer has spent time looking at it carefully.
Verification is the step that closes the loop. It is the moment where you find out whether what was generated actually reflects the design, rather than assuming it does because the AI had access to the right inputs.
In a workflow that includes verification, the cycle looks like this:
The verification step does not need to be slow. It does need to be deliberate.
Verification adds a step to a workflow that AI tools made faster, and adding steps to fast workflows feels like moving backward. The implicit assumption is that because the AI had access to the design, the output should reflect it.
That assumption is reasonable and often mostly true. The issue is that "mostly" is doing a lot of work. A page that is 90% accurate has real deviations, and without a verification step there is no systematic way to know where they are or how significant they are.
The teams that get the most out of AI-assisted development are the ones that treat verification as the natural end of the generation step, not as an optional extra that gets added when someone notices something looks off.
