Web & Digital · Organization & Productivity
Bex Jeanson: a Figma Make prototype exposed product gaps, and an OOUX sprint filled them
A staff product designer prototyped an automation MVP in Figma Make, hit undefined system questions, ran an OOUX sprint, then re-prompted with its outputs.
Done withFMFigma Make
Author’s account; no screenshots published
WHAT THEY DID
Staff product designer Bex Jeanson prototyped MVP, MVP+ and future versions of an automation experience in Figma Make after an MVP workshop. The prototype raised questions nobody had answered, such as where notifications live and whether triggers are steps, so she ran a multi-week object-oriented UX (OOUX) sprint with a designer she mentored.
STEP BY STEP
- Prototyped quickly in Figma Make right after the MVP workshop to test a proof of concept.
- Used what the prototype could not answer (notification level, trigger vs step) as a list of system questions.
- Ran an OOUX sprint: listed nouns in their own and competitors' systems, defined core objects and relationships, documented actions, and filled a CTA inventory that surfaced permissions and roles.
- Used AI for generative listing only, and kept relationship decisions with the team.
- Re-prompted Figma Make with object wireframes, the OOUX outputs, a sitemap and parity requirements to build a consistent vision prototype.
HOW IT WENT
The sprint defined objects, relationships, actions and a CTA inventory that surfaced permissions and roles. She fed the object wireframes, the OOUX outputs, a sitemap and parity requirements back into Figma Make prompts.
Reported results: She reports leadership shared the vision prototype among themselves, and months later her team used the same object model to keep another team's embedded automation consistent.
WORTH KNOWING
This is her own account, with no screenshots of the prototype or the model. She says AI helped list nouns, verbs and missed items but struggled with relationship decisions, sometimes contradicted itself, and didn't save time on key product decisions. The AI used for that listing isn't named, and the work date isn't stated.
TRY IT
- Before prompting Figma Make on a complex product, list its objects, relationships and actions, and give it those plus simple object wireframes, a sitemap and the features you must keep.
- Treat a fast AI prototype as a question-finder: write down every decision it makes you guess.
- Let AI brainstorm nouns and verbs; make relationship and permission decisions yourself, in short sessions (she kept them to two hours or less).
Rates the entry, not the tool.
Source: Bex Jeanson · dscout (People Nerds)Open as a page
- Work done: not stated in the source (The article links her earlier dscout piece on the same project, dated 5 Mar 2026.)
- Source published: Sep 3, 2026
- On 4TC: added Oct 27, 2026 · checked Oct 27, 2026
About this source
Type: First-person workflow · Firsthand report
- When Rapid Prototyping with AI Causes Gaps, Try This Method
Bex Jeanson · dscout (People Nerds) · Published Sep 3, 2026 (from the byline and the page's JSON-LD datePublished) · Checked Oct 27, 2026
Source check: Reviewed by a 4TC editor on Oct 27, 2026. Claims, quotes and dates re-checked against the sources by Ella on 27 Oct 2026. Nobody at 4TC has tested this.
Claims and where they come from
- After an MVP workshop, the author used Figma Make to explore MVP, MVP+ and future versions of an automation experience. The prototype exposed undefined questions, e.g. where notifications live and whether triggers are steps. (reported) [1]
- She ran a multi-week object-oriented UX (OOUX) sprint in sessions of two hours or less with a designer she mentored, defining objects, relationships, actions and a CTA inventory. (reported) [1]
- She reports AI helped generate nouns, verbs and missed items but struggled with nuanced relationship decisions and sometimes contradicted itself. (reported) [1]
- She fed object wireframes, every OOUX output, a sitemap and parity requirements into Figma Make prompts. She reports leadership shared the resulting vision prototype, and months later her team used the model to keep another team's embedded automation consistent. (reported) [1]
4TC interpretation
Our reading, not something the source says.
A concrete case where fast AI prototyping was useful because it exposed missing design thinking early, plus a repeatable fix: model the system, then prompt with the model.
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