Product teams often use placeholder text and duplicate UI components, but users don't provide good feedback on unrealistic designs. A prototype with authentic, varied content—even if the UI is simpler—will elicit far more valuable user feedback because it feels real.
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Instead of guarding prototypes, build a library of high-fidelity, interactive demos and give sales and customer success teams free reign to show them to customers. This democratizes the feedback process, accelerates validation, and eliminates the engineering burden of creating one-off sales demos.
Many teams wrongly focus on the latest models and frameworks. True improvement comes from classic product development: talking to users, preparing better data, optimizing workflows, and writing better prompts.
The "Owner's Delusion" is the inability to see your own product from the perspective of a new user who lacks context. You forget they are busy, distracted, and have minimal intent. This leads to confusing UIs. The antidote is to consciously step back, "pretend you're a regular human being," and see if it still makes sense.
The data-driven prototyping approach separates the UI from the content. This enables rapid iteration, allowing you to generate entirely new versions or localizations of a prototype (e.g., a trip to Thailand instead of Paris) simply by swapping a single JSON data file, without altering any code.
Developers often test AI systems with well-formed, correctly spelled questions. However, real users submit vague, typo-ridden, and ambiguous prompts. Directly analyzing these raw logs is the most crucial first step to understanding how your product fails in the real world and where to focus quality improvements.
The most effective product reviews eliminate all abstractions. Forbid presentations, pre-reads, and storytelling. Instead, force the entire review to occur within the actual prototype or live code. This removes narrative bias and forces an assessment of the work as the customer will actually experience it.
Historically, resource-intensive prototyping (requiring designers and tools like Figma) was reserved for major features. AI tools reduce prototype creation time to minutes, allowing PMs to de-risk even minor features with user testing and solution discovery, improving the entire product's success rate.
For complex features, a 17-page requirements document is inefficient for alignment. An interactive AI-generated prototype allows stakeholders to see and use the product, making it a more effective source of truth for gathering feedback and defining requirements than static documentation.
Instead of providing a vague functional description, feed prototyping AIs a detailed JSON data model first. This separates data from UI generation, forcing the AI to build a more realistic and higher-quality experience around concrete data, avoiding ambiguity and poor assumptions.