Your input goes straight to the people building this product. No right or wrong answers.
Responses are stored locally and exported to the research team as a spreadsheet.
All fields are optional. Skip anything you'd prefer not to share.
Select how much you agree with each statement. 1 = Strongly Disagree, 7 = Strongly Agree.
Write as much or as little as you like. No answer is required.
An open space. No prompt, no rules.
Your answers so far
Your feedback has been recorded and will be reviewed by the research team. You've helped make this product better for everyone who uses it.
UX researchers are typically constrained to one or two active projects at a time. Prompt engineering changes that equation.
This tool was built through natural-language instructions to an AI, with minimal manual coding. The same approach can produce MVPs, prototypes, and internal research tools that extend your team's reach across an entire organisation.
When a UX researcher learns prompt engineering, they can build functional tools without waiting for a development team. Those tools get deployed across departments, where colleagues collect feedback and export it for analysis or AI processing.
The result is expanded research capability across the organisation, even when the researcher is fully occupied. Reading and adjusting code amplifies this further, giving you precision that re-prompting alone cannot always guarantee.
I described the tool in plain English: a multi-step feedback form, a 7-point Likert scale modelled on the PSSUQ, four prompted qualitative questions, and a one-click Excel export. No wireframes. No technical requirements document.
After each version I applied the same critical eye I would to a prototype in usability testing. I flagged what was unclear, what felt redundant, and what was missing. Each prompt built directly on the last.
I asked the AI to apply Nielsen Norman's 10 Usability Heuristics and Jakob's Law throughout. The result: a familiar wizard pattern, a persistent progress bar, inline error recovery, and a review screen before submission.
Every response field maps directly to a spreadsheet column. The export produces a clean .xlsx file that any team member, or an AI model, can open and analyse with no researcher involvement required.
A tool like this can be installed on any shared cloud environment and handed to product, operations, or communications teams. They collect structured user feedback independently while the researcher focuses on higher-order work. That is UX maturity scaling without adding headcount.
Basic coding literacy is a genuine advantage. When AI output is close but not quite right, adjusting a few lines directly is faster and more precise than re-prompting from scratch. That precision is something another round of prompting cannot always guarantee.
"Prompt engineering lets UX researchers punch above their weight. You can build research tools, prototype workflows, and deploy them across teams, all without waiting for a development sprint."Jean Pierre, UX Researcher
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