Free Review Schema Generator for AI Search
Generate JSON-LD Review schema with ratings, reviewer, and review body. Optimized for ChatGPT, Google AI Overviews, and star-rating rich results. No sign-up.
Review Details
Review SchemaThe name of the thing being reviewed.
Use the item's canonical name — the string users would search for.
What category of thing you're reviewing.
Pick the most specific type — Product for goods, LocalBusiness for venues, Book for books, Movie for films.
URL of the item being reviewed (optional, but strongly recommended).
Linking to the reviewed item lets AI correlate your review with the entity. Skip this and the review floats free.
Name of the person writing the review.
Named reviewers are trusted more than anonymous ones by AI engines. Use a real person, not 'Customer #1234'.
The reviewer's rating (1–5 by default, but see bestRating).
Maximum on your rating scale. 5 for 5-star, 10 for 10-point, 100 for percentage.
Minimum 50 characters. AI engines quote review bodies directly.
0 / 5000Mention specific attributes (build quality, sound, service, etc.). Generic reviews don't get cited.
When the review was published.
Organization publishing the review (optional).
{
"@context": "https://schema.org",
"@type": "Review",
"itemReviewed": {
"@type": "Product",
"name": "Item name"
},
"author": { "@type": "Person", "name": "Reviewer" },
"reviewRating": {
"@type": "Rating",
"ratingValue": 4.5,
"bestRating": 5
},
"reviewBody": "Review text…"
}
AI Readiness Score
Get a 0–100 score showing how likely AI search engines are to parse and cite content from your page.
How to use this code:
- Copy the JSON-LD code above
- Paste inside
<script type="application/ld+json"> - Add the script tag to your review page
<head> - Validate with Google Rich Results Test ↗
See how ChatGPT answers — with and without your schema
Side-by-side comparison of what an AI assistant would return for a typical review query, with vs. without the structured data you generated above.
Sample user query
“Is this item worth buying? What do reviewers say?”
Click Simulate to see the AI response without schema.
Click Simulate to see the AI response with schema.
How to install this schema on your site
Pick your platform. Every step includes a copy-paste-ready code block where it applies.
- 1
Install and activate a schema plugin: "Rank Math SEO", "Yoast SEO Premium", or "Schema Pro". For straight JSON-LD injection without a full SEO plugin, "WPCode" (formerly Insert Headers and Footers) is the simplest option.
- 2
In Rank Math: go to the post edit screen → Rank Math sidebar → Schema tab → Schema Builder → paste your JSON-LD. In Yoast Premium: the Schema tab auto-generates standard types; use the Custom Schema plugin add-on to inject custom types.
- 3
For WPCode: Code Snippets → Add Snippet → Custom Code (HTML/PHP) → paste the full <script type='application/ld+json'> tag. Set Location to 'Site Wide Header' or 'Specific Post/Page' depending on scope.
html<script type="application/ld+json"> { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [ /* your Q&A pairs */ ] } </script> - 4
Publish the post. Open the live URL, view source, confirm the <script type="application/ld+json"> tag appears in the <head>.
- 5
Validate at search.google.com/test/rich-results.
What is Review Schema?
Review is the Schema.orgtype for a specific person's or publication's evaluation of something — a product, a business, a book, a movie, an event, or any other reviewable entity. It connects three entities explicitly: what is being reviewed (itemReviewed), who is reviewing it (author), and the numeric rating plus written reasoning (reviewRating and reviewBody). When AI search engines surface reviews in recommendation queries, they're reading this exact structure.
A companion type, AggregateRating, summarizes many Reviews into a single average rating and review count. Most product and business pages use both: AggregateRating for the star-display summary at the top, plus a selection of individual Reviews to provide qualitative context. Here is a canonical editorial Review:
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@type": "Review",
"itemReviewed": {
"@type": "Product",
"name": "Acme Wireless Headphones"
},
"author": {
"@type": "Person",
"name": "Jane Doe"
},
"reviewRating": {
"@type": "Rating",
"ratingValue": 4.5,
"bestRating": 5
},
"reviewBody": "The noise cancellation is excellent...",
"datePublished": "2026-01-15"
}
</script>Notice the nested itemReviewed uses its own @type — in this case Product, but it could just as easily be LocalBusiness, Book, Movie, SoftwareApplication, or any other reviewable schema type. This nesting is what lets AI engines correctly attribute the review to the specific entity.
Why Review Schema Matters for AI Search
Review schema has become even more valuable since the shift to AI-powered search. When a user asks Perplexity "what are the best 4K TVs under $1000," the AI synthesizes recommendations from review content across the web — and the reviews that make it into the synthesis are overwhelmingly ones with clean Review schema. A thoughtfully reviewed product with no schema is invisible to AI recommendation engines; a mediocre review with perfect schema will get cited.
AI engines reward specific properties of Review schema:
- Named reviewer authority: A Review attributed to a named Person with a sameAs-linked identity (LinkedIn, bio page, other review authorship) dramatically outperforms anonymous reviews. Editorial reviews from recognized publications carry the most weight.
- itemReviewed disambiguation: Linking the review to the correct entity via itemReviewed.url or sameAs is what lets AI engines confidently attribute your review to the right product or business. Ambiguous items get filtered out.
- Rating precision: A rating of 4.7 is more informative than a rating of 5. AI engines treat rounded ratings as lower-quality signals than precise ones, all else being equal.
- Review body substance: AI engines quote specific phrases from reviewBody directly in their recommendation responses. Reviews with concrete observations ("the bass is tight, the mids are recessed") get quoted; vague ones don't.
- Recency: Recent reviews weight more heavily in time-sensitive categories. A 2025 review of a 2026 product model gets the latest context; a 2018 review doesn't.
- Publication credibility: Reviews with a named Publisher Organization that AI engines recognize as credible get a citation multiplier. This is how Wirecutter consistently beats random review aggregators.
The punchline: for review publications, Review schema is not optional. It is the entity declaration that makes your review discoverable by AI systems that increasingly bypass traditional search.
Review Schema Best Practices
Always link itemReviewed by URL
A Review of "Acme Headphones" is weaker than a Review of the specific Acme Headphones product page. URL linking eliminates ambiguity.
Attribute to a real Person
Anonymous or "Staff" reviews lose weight. Named reviewers with bios and external profiles earn citation.
Use precise ratings
4.7 beats 5. AI engines treat precision as a quality signal.
Write substantive reviewBody
Name specific attributes — sound, build, fit, service, taste. AI engines quote these phrases directly.
Include date stamps
datePublished on every review. Freshness matters, especially for fast-evolving categories.
Don't self-review
Google's guidelines prohibit it, and AI engines detect and discount self-reviews.
Combine Review with AggregateRating
Both together — summary stats plus individual voices — is the strongest pattern.
Scope to actual content
The review schema must match the review visible on the page. Hidden schema is manipulation.
How to Install Review Schema
Editorial review site
- 1Structure your CMS so each review article stores itemReviewed data explicitly — don't rely on free-text identification.
- 2Generate Review schema at build time from the structured review record.
- 3Always emit datePublished and dateModified; update the latter when you refresh the review.
- 4Attribute to a named Person entity, and include a sameAs Person profile on that author's bio page.
- 5Validate the review page URL with Google Rich Results Test.
E-commerce product page (with user reviews)
- 1Use AggregateRating for the summary stats visible at the top of the product page.
- 2Emit 5–10 individual Review objects for the most helpful reviews.
- 3Pick helpful reviews deterministically (by upvote count) so schema is cache-friendly.
- 4Never expose reviews in schema that aren't also visible on the page.
- 5Validate a sample of product pages to ensure both AggregateRating and Review schema render correctly.
WordPress
- 1For review blogs, plugins like WP Review Pro, Rank Math, or Schema Pro auto-generate Review schema from custom fields.
- 2For WooCommerce stores with user reviews, WooCommerce itself handles AggregateRating; augment with review-specific plugins if you need individual Reviews in schema.
- 3Validate on the live URL — WordPress schema plugins sometimes emit Review schema without itemReviewed, which fails validation.
- 4Ensure review dates come from the post or comment timestamp, not the page rendering time.