Review and rating schema for AI search

What review schema does

Review schema marks up an individual review; aggregateRating summarizes many into an average and count. Together they make rating trust signals machine-readable, so engines can factor them into answers and potentially display them. For decisions where reputation matters, genuine ratings are a strong corroborating signal - and marking them up cleanly helps engines use them.

The strict rules (because it's abused)

Review markup is heavily policed - stay strictly within the rules:

• Only mark up reviews and ratings genuinely displayed on the page.
• Don't mark up self-serving ratings of your own business on your own site where that's disallowed.
• Never invent ratings, inflate counts, or mark up hidden data.
• Match the aggregateRating value and count to what's actually shown.

Why honesty is enforced here specifically

Fake and self-serving review markup was so widely abused that engines tightened the rules and actively penalize violations. Because ratings directly influence trust and clicks, the incentive to cheat is high - and so is the scrutiny. Engines corroborate ratings against other sources, so inflated or invented ones fail and damage trust across your site. Genuine reviews, marked up accurately, are the only version that works.

Implement and validate

Use JSON-LD, mark up only real displayed reviews with accurate values, follow the current eligibility rules for your content type, and validate with a structured-data testing tool. When done right, review schema reinforces a genuine trust signal; the moment it drifts from the visible, honest reality, it becomes a liability rather than an asset.