Misconception Generator
Find the expectation gap before users do.
A small Gemini-powered experiment that turns an AI feature description into three potential user misconceptions, helping designers refine capability explanations and guidance.
THE DESIGN QUESTION
What might a reasonable user believe this AI feature can do that its actual behavior does not support?
Describe the feature and ask the model to propose likely misconceptions in the user’s voice, paired with a clarification. Treat the results as research hypotheses.
- My contribution
- The feature-to-misconception interaction, role prompt, response format, and streaming presentation.
- Current scope
- Local prototype · single model call
- Exploring
- Prompt design · Expectation setting · Streaming
THE WORKFLOW
Describe
Provide a specific feature and its actual limits.
Generate
Request three potential mistaken expectations.
Clarify
Pair each expectation with the relevant capability boundary.
Check
Review against the real product and validate with users.
A CLOSER LOOK
A feature description is also an expectation contract.
Authored illustration · not a captured model response or research finding
An image tool offers “Edit the text.”
In this fictional example, the tool regenerates the full image and does not edit a text layer.
“Only the words will change.”
A user may reasonably expect the layout and other image details to remain untouched.
“This edit regenerates the image.”
Explain that surrounding details may change, and retain the original for comparison.
Check the interpretation with real users.
Ask people what they expect to happen before the edit, then compare their expectation with the output.
The illustration explains the method. Actual suggestions require review against the product’s real behavior and evidence from users.
UNDER THE SURFACE
Make the process legible.
Make the boundaries explicit.
Prompt design
A role prompt frames the model as an AI UX researcher and requests three concise misconceptions with clarifications.
Streaming
Gemini output is streamed into the interface so users can read it as it arrives.
Scope
This is a single model call, without a tool-execution loop or multi-agent orchestration. Format requirements are prompt instructions, not schema guarantees.
TODAY
Local prototype
The feature-description input and Gemini streaming endpoint are implemented in a local prototype.
WHAT’S NEXT
Still exploring
Improve the interface and compare suggestions with observed user research before opening a hosted trial.
Model-generated suggestions are hypotheses, not research findings. No user-study validation or measured accuracy is claimed.