Generated-Data Grounding Check
Challenge generated dashboards with current facts before trusting their data
- Difficulty
- Easy
- Time to result
- ~days to results
- Steps
- 5
- Confidence
- 93%
When an AI design tool is asked to populate a dashboard with live information, treat grounding as an unproven capability rather than an assumption. Explicitly request current internet data, then identify known facts that should be present if retrieval worked, such as current model names, versions, dates, or rankings. Inspect the generated interface for stale markers that reveal reliance on training data. If the first attempt fails, provide a specific source page and repeat the test to determine whether the tool can extract its contents. Compare the result again instead of trusting the tool's visual confidence. If the displayed data remains stale, classify the artifact as a design prototype and implement a separate API, database, or retrieval layer before using it operationally.
Origin
Extracted from Marketing Against The Grain during Matt Wolf's live attempt to make Stitch ground an LLM comparison dashboard in current web data.
Core principles
- 01Visual polish does not prove factual accuracy
- 02Test grounding with independently known current facts
- 03Inspect outputs for stale names, dates, and versions
- 04Distinguish design generation from data retrieval
- 05Plan a real data integration when grounding fails
How to run it
- 1
Request grounded information
Tell the generator to use actual, current internet data for the proposed dashboard. Make the freshness requirement explicit rather than assuming it is understood.
Pro tip Specify the type of facts and the relevant present-day scope.
Watch out An instruction to use the internet does not prove that the tool has browsing or retrieval access.
- 2
Define freshness markers
List several independently known names, versions, dates, or values that a current result should contain. These markers form a quick acceptance test.
Pro tip Choose markers that changed after the model's likely training cutoff.
Watch out A single correct item may be a coincidence or memorized fact rather than evidence of grounding.
- 3
Inspect the generated output
Compare the displayed content with the freshness markers and look for obsolete terminology. Treat stale values as evidence that the tool relied on training data or placeholders.
Pro tip Check both prominent labels and less visible supporting values.
Watch out Professional visual design can make incorrect information feel more authoritative.
- 4
Test a supplied source
Give the tool a specific website containing the desired data and regenerate the design. Verify whether the values now reflect that source.
Pro tip Use a source whose current contents you can independently inspect.
Watch out Matching a site's visual style does not demonstrate that the tool extracted its underlying data.
- 5
Classify and remediate
If grounding remains unverified, label the result as a static design prototype. Add a separate data source, API, or retrieval mechanism during implementation.
Pro tip Keep useful layouts while replacing generated values with controlled data bindings.
Watch out Do not publish stale generated values as factual comparisons.
In the wild
Matt explicitly asked Stitch to pull actual internet data for a dashboard comparing leading language models. He then noticed obsolete model names and dates, concluding that the tool was using older training data. Supplying a website did not cause the dashboard to reflect that site's current information, so he treated the result as a design rather than a grounded data product.
→ The prototype's visual value was preserved while its generated data was correctly rejected as unreliable.
A team asks an AI tool to generate a competitor-pricing dashboard, then checks the displayed plans against current vendor pages. When several discontinued prices remain after a source URL is supplied, the team retains the interface design but connects it to a maintained pricing database.
→ The team avoids presenting stale prices while still benefiting from rapid interface generation.
Common mistakes
Trusting polished placeholders
A coherent dashboard can contain invented or obsolete values. Evaluate factual content independently from visual quality.
Treating URL access as data access
A tool may imitate a website's colors and fonts without extracting its current content. Verify specific values after supplying the source.
Checking only one fact
One apparently current value is weak evidence of grounding. Require multiple independent freshness signals before trusting the output.
Is it for you?
Best for
It is best for anyone prototyping dashboards, comparisons, reports, or interfaces whose usefulness depends on changing external information.
Not ideal for
It is unnecessary for purely fictional mock-ups where every label and value is intentionally placeholder content.
From the transcript
“And the real double check is did it actually ground the information in the screenshot from real web data?”
“So it's pulling from its existing training data. So it didn't look like it grounded from the internet because we can see this is all…”
“it can't actually get data from this page by looking at it.”
From the episode
I Replaced a $5,000 Designer with Google Stitch