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Review restaurant photographs for the right expectations

Photographs can communicate a room, a dish or an event setup more quickly than a paragraph.

01

The decision this workflow supports

Photographs can communicate a room, a dish or an event setup more quickly than a paragraph. They can also imply that an old presentation or special arrangement is a standard current offer. Review what each image suggests.

02

Put the review process in place

Ask the content owner what the photograph is meant to illustrate and whether the scene still represents the restaurant. Use captions for dated examples or special events. Send questions about menu details back to staff instead of describing ingredients or suitability from the photograph.

03

Worked example for your team

A fictional private-events photograph shows decorations supplied for one past gathering. Without a caption, a visitor may assume they are included in every event. Ask the events owner what the image should illustrate and label it accordingly. If the space has changed since the photograph, decide whether the image still helps explain the current venue. The review is about the expectation created by the whole page, not only whether the image file is attractive. Any description of food or facilities must come from approved information rather than visual inference.

04

Sample wording to approve

“This photograph illustrates a previous setup. Please ask the restaurant team about current options for your date; the image does not confirm a menu, seating arrangement or event package.” This wording is a draft for the restaurant owner to approve before use.

05

Fields for the staff handoff

Staff need image location, intended purpose, current or example status, caption owner, and review date. Mark uncertain details explicitly and keep the requested action separate from anything restaurant staff have already confirmed.

06

Acceptance tests before use

A previous event is not presented as guaranteed setup. A dish photo does not generate ingredient claims. Staff can identify the image when a guest asks about it. Record the observed result, assign any mismatch, and repeat the affected scenario after correction.

Further reading

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