AI B2B Design: 4-Step Intent Prototype Fix
Stop vibe-coding chaos—use 4-step Intent Prototyping to turn AI into a disciplined builder of stable, testable B2B products.
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In traditional design workflows, particularly during the Solution Discovery phase, designers typically begin with sketches, hand-drawn flowcharts, and diagrams, gradually progressing toward high-fidelity prototypes.
However, in reality, teams often concentrate excessive energy on visual presentation, because stakeholders typically respond most quickly to aesthetically pleasing interfaces. In contrast, truly critical but less intuitive elements, such as conceptual models and business processes, are frequently overlooked.
The result is that many teams ultimately deliver only a series of static images. During user testing or validation, participants can only observe without interacting; during engineering handoff, developers are forced to rely on these static images to speculate about the logic behind the design, leading to misunderstandings and implementation deviations.
The Pitfall of Vibe Coding
With the advancement of AI technology, a more immediate and appealing approach has gradually gained popularity: vibe coding. This method allows non-technical personnel to quickly obtain interactive interface prototypes through natural language descriptions of requirements—highly tempting. However, the problem is that it easily conceals big, systemic risks.
When expressing requirements solely through natural language, you haven’t truly constructed a clear system architecture. Many design intentions remain implicit and ambiguous, while AI makes inferences based on context—but its inferences aren’t necessarily accurate. For example, the author once wanted to create a tool for tracking product idea validation, initially proposing the need to record “hypotheses, experiments, dates, and status.”
The AI promptly generated an interactive prototype. But when he subsequently requested expanded functionality—such as associating specific product ideas with each experiment, or calculating ICE scores (Impact, Confidence, Ease)—the system began experiencing broken relationships, blank pages, and data loss. The AI “fixed bugs, but there were actually no programming errors”—rather, the issue stemmed from the lack of a clear conceptual model in the initial phase.