Responding to Online Reviews
Reply to praise and criticism consistently.
Added October 15, 2026. Written with AI assistance and checked for clarity and placeholders; not reviewed by a lawyer, accountant, clinician or other professional. How these prompts are made
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[VERIFY] is an editorial flag, not a blank to fill. Wherever the prompt shows [VERIFY], check that item against a reliable source before you use it (what [VERIFY] means).
Write review responses. Restaurant and style: [RESTAURANT_STYLE]. Examples of reviews I received (positive, mixed, negative): [REVIEWS]. Facts I can confirm about each situation: [FACTS]. Draft replies of 40 to 90 words for each review: thank, acknowledge specifics, address concerns without arguing, offer to continue offline, and avoid sharing personal details. Provide a decision flow for reviews that appear fake or abusive (report through platform tools, do not engage), a policy for never offering compensation in exchange for removing a review, and a template for replying to health-related complaints that asks the guest to contact the manager privately and escalates according to the food safety procedure [VERIFY]. Add a monthly summary template of themes. Also add a worked example of replies to three invented reviews, labelled as an illustration, and a tracking sheet that records the date, platform, rating, theme and action. Never promise a date, discount or outcome I have not confirmed. Mark deposit, fee and refund rules [VERIFY] with [JURISDICTION] and say which official body publishes the current rule.
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Review (invented, 2 stars): 'Waited 40 minutes and the food was cold.' Reply: 'Thank you for telling us, and I am sorry about your wait and the cold food. That is not the standard we aim for. We looked at Saturday's service and found a staffing gap in the kitchen. Please contact me at [email] so I can invite you back. Maria, Manager.' Positive review reply: 'Thank you, Tom! I will tell the team about your compliments on the lamb.' Do not offer compensation in exchange for removal.
Illustrative only, made-up data. Your results will differ and must be reviewed.