Verified against ChatGPT · 2026-08-09
Wireframe a dashboard around the three-second glance test, not a grid of equal-weight widgets
Designs a dashboard's information hierarchy around the single question a user opens it to answer, ranking every widget by whether it earns a place in the first glance, a quick scan, or a click-through.
The prompt
Ready to copy — highlighted parts are example details you can swap.
Wireframe the dashboard described below. Most dashboard wireframes default to a grid of equal-sized widgets, which fails the actual test a dashboard has to pass: a user glancing at it for three seconds should immediately know the one thing they came to check. DASHBOARD PURPOSE A support-team lead's daily view of ticket backlog health. WHO OPENS THIS AND WHY Support team lead, opens it first thing each morning to decide if they need to reassign tickets before the day starts. DATA POINTS AVAILABLE Open ticket count, average response time, tickets past SLA, agent-by-agent load, weekly trend chart, customer satisfaction score, ticket tags breakdown. MOST COMMON FOLLOW-UP ACTION Reassigning tickets from an overloaded agent to one with spare capacity. STEP 1 — RANK EVERY DATA POINT BY GLANCE TIER Sort every listed data point into exactly one of three tiers: Glance (must be readable in three seconds without any interaction — this tier should have at most two or three items, since a glance tier crowded with ten equally-sized numbers defeats its own purpose), Scan (readable within about ten seconds of active scrolling or reading, supporting detail), or Click-through (available but tucked behind a click, drill-down, or secondary tab). Justify each placement by what the user in Support team lead, opens it first thing each morning to decide if they need to reassign tickets before the day starts. is actually trying to find out, not by how interesting the data point is on its own. STEP 2 — WIREFRAME THE LAYOUT Produce a text-based grid wireframe (rows and columns of labeled blocks with relative sizing noted, e.g. [BIG: primary metric] [small: secondary metric] [small: secondary metric]) that gives Glance-tier items the largest visual weight and top-left-to-top-right reading priority, Scan-tier items visible but smaller, and Click-through items represented only as a labeled entry point (a tab, button, or link), not rendered in full. STEP 3 — VALIDATE AGAINST THE FOLLOW-UP ACTION Check that the most common follow-up action has a visible, one-click path from the dashboard's main view. If it doesn't, flag this as a structural gap and propose where to add the entry point. WHAT NOT TO DO Do not default to a uniform grid where every widget gets equal size regardless of tier. Do not include a data point in the Glance tier just because it's easy to visualize (like a pie chart) if it isn't actually what the user opens the dashboard to check. OUTPUT FORMAT The three-tier ranked list with justifications, then the text wireframe, then the follow-up-action validation note.
Customize
Optional — swap in your own details for the highlighted parts above.
Why this works
Asked to wireframe a dashboard without a hierarchy constraint, GPT-5.1 tends to produce a uniform grid where every available data point gets roughly equal visual weight, because a flat list of metrics has no inherent ranking signal in the prompt and the model has no reason to privilege one box over another — the three-tier sort is the mechanism that forces an actual prioritization decision, and capping the Glance tier at two or three items is what prevents the model from simply relabeling most of the grid as "glance" and reproducing the same undifferentiated layout under a new name. Anchoring every tier placement to the specific user and trigger, rather than to which data point is most visually interesting to chart, counters a real bias in language models toward foregrounding whatever data is easiest to render attractively (a pie chart, a trend line) even when it isn't what the stated user actually needs first — a ticket-backlog lead opening the dashboard to catch an SLA breach doesn't need agent workload displayed as prominently as the count of tickets past SLA, even though a workload breakdown might make for a more visually rich widget. The follow-up-action validation step exists because a dashboard's job doesn't end at display — it's supposed to lead into an action — and a wireframe that nails information hierarchy but leaves the most common next action two clicks deep has still failed at its actual purpose; checking for this explicitly, as a distinct final step rather than hoping it falls out naturally from the layout, is what catches the gap before it reaches a designer.
What you get back
Glance tier: Tickets past SLA (this is the number that determines whether reassignment is needed today), Open ticket count. Scan tier: agent-by-agent load, average response time. Click-through: weekly trend chart, satisfaction score, tag breakdown. Layout: top row is one large SLA-breach counter with a red/green state indicator, next to open-ticket total; second row shows a compact per-agent load bar; a 'View trends' tab sits in the top-right for click-through items. Follow-up validation: reassigning an overloaded agent's tickets isn't reachable from the main view — add a one-click 'Reassign' action directly on each row of the per-agent load bar.
Verified against
ChatGPT GPT-5.1 · 2026-08-09
Changelog
- 2026-08-09 — Initial publish, verified against ChatGPT GPT-5.1.
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