Product demos & UI / Guide

Use fictional data without making the demo misleading

Fictional data can make a product task easier to understand, provided the audience can recognize it as an example. Invent the records needed to explain the workflow, then verify that the interface behavior shown with those records is real or clearly identified as a concept.

By AnimroUpdated 3 min readAI-assisted · independently agent-reviewed

Who this is for: Marketers and product teams replacing sensitive records in demonstration assets.

What to prepare

  • The exact workflow to demonstrate
  • A small fictional dataset with stable identifiers
  • A visible sample-data label
  • A continuity sheet for changed fields and totals
  • Verified screenshots of actual product behavior

Decide what is fictional and what must be verified

A sample customer's name, a request subject and a deliberately small dataset may all be invented for clarity. Whether a filter changes the list, a role can approve a request or a save persists is a product fact. Do not let an attractive invented dataset substitute for evidence of those behaviors.

Use a visible label such as 'Illustrative data' near the dataset or in a persistent explanatory area. A disclosure only in a production note will not help a viewer. For prototype imagery, use a separate 'Prototype concept' label; sample data labeling alone does not disclose an unbuilt interface.

Worked example: a four-request review queue

The following records are entirely illustrative. They support a demo of filtering for requests awaiting review, without claiming any customer usage or business results. Use neutral names that cannot be mistaken for endorsements.

  • R-014: 'Sample packaging'; owner Example author A; status Awaiting review; submitted 2 October in the example timeline.
  • R-015: 'Sample label'; owner Example author B; status Draft; submitted date blank because it has not been submitted.
  • R-016: 'Sample instruction card'; owner Example author A; status Awaiting review; submitted 3 October in the example timeline.
  • R-017: 'Sample insert'; owner Example author C; status Approved; submitted 1 October in the example timeline.
  • Expected filtered view: R-014 and R-016. Expected count: 2. Detail screen for R-014 repeats its subject, owner, status and example date exactly.

Track every dependent value across scenes

If the demonstration approves R-014, update its detail status and check how the real queue responds. An Awaiting review count should then reflect the actual product behavior for this dataset. Do not leave a convenient count of 2 on one screen while showing only one remaining matching record elsewhere.

Maintain a small continuity sheet with each field's starting value, action and resulting value. Stable identifiers are especially useful when names wrap or a list reorders. If the UI masks identifiers, preserve another recognizable combination, such as subject and owner, rather than adding a product field that does not exist.

Keep the example from becoming a performance claim

A chart built from invented requests illustrates how to read a chart; it does not establish reduced workload, faster approvals or customer growth. Describe the demonstrated action precisely: 'Filter the queue to two example requests.' Avoid turning those sample values into a headline about what a buyer will achieve.

When adapting this plan for financial, health or other consequential records, keep the task focused on interface explanation and have the appropriate owner inspect the content. Replacing names alone may leave revealing notes, identifiers or attachments. Review the entire frame and every linked detail screen before publication.

Review checklist

  • Viewers can see that the records and values are illustrative.
  • Counts, filters and detail screens agree with the same sample records.
  • Fictional values are never presented as customer outcomes.
  • Prototype status is disclosed separately from sample data.
  • Replacement covers notes, attachments and overlays as well as names.
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