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Module 03 / Lesson 18 · Read, apply, check
Match structured data to visible facts and distinguish technical validity from eligibility and actual search display.
Structured data expresses information in a machine-readable form. Start with the visible page and choose a supported type that accurately describes it. Do not add invented reviews, offers or author details simply because the markup permits a field. The underlying facts and the user-visible content must agree.
Search features have their own required properties, content conditions and policies. A syntactically valid object can still be ineligible for a particular enhancement. Read the current documentation for the feature and page type you are implementing. Generic validation alone cannot tell you whether the page satisfies every relevant requirement or whether the feature is available in that setting.
Use suitable validation tools, then inspect representative pages manually. Verify prices, availability, names and URLs against the rendered content. Check how the template behaves when a field is missing or a product changes. Errors often arise from stale data and template assumptions rather than from punctuation in the markup.
Eligibility does not guarantee that a rich result will appear for every query, device or user. Monitor available reports and correct genuine issues, but avoid promising a special search treatment. Google does not require a special AI schema to appear in its AI search experiences. Helpful, accessible content remains the underlying priority.
Markup identifies that same product, not a hidden unrelated item.
Supported price fields use the same accurate source of truth.
Review-related markup follows the specific feature’s content policies.
Passing validation supports diagnosis; eligibility still does not guarantee display.
Hypothetical example: an online shop adds product structured data to a template. The markup validates, but a discounted item shows yesterday’s price in the data while the visible page shows today’s price. The SEO lead asks the developer to connect both outputs to the same current product record and tests discounted, unavailable and regular items. The team checks the feature-specific requirements and monitors reported issues after deployment. It describes the work as improving accurate machine-readable product information and potential eligibility. It does not sell the change internally as guaranteed rich-result placement, guaranteed stars or a special route into AI answers. The correction addresses a real mismatch that a syntax-only check would miss.
A service business adds five-star review markup using testimonials that are not eligible for the intended search feature. The code may be well formed, but the implementation does not become appropriate merely because a validator can parse it. The team needs to review the specific policy and remove unsupported claims or unsuitable markup rather than repeatedly changing the formatting until a preferred display appears.
Misconception: valid structured data guarantees enhanced search presentation. Correction: validity, policy eligibility and actual display are separate stages. A tool can confirm selected technical properties without promising how a search system will present the page.
Supported features and requirements change. Maintain an inventory of implemented types and their owners, especially on templates that affect many pages. A useful implementation should survive ordinary content updates without creating misleading machine-readable claims.
Do this today · 30 minutes
Choose one page and map each proposed structured-data field to a visible, verifiable fact. Check the current feature guide, validate the implementation and write a monitoring plan that distinguishes errors from absence of display.
Use an authorized page or campaign relevant to this lesson, or work entirely from the illustrative sample below. Record proposed changes as drafts until they are approved and implemented.
A product page visibly offers a blue lamp for £45, but proposed markup says £40 and uses an unsupported rating.
Before: Syntactically valid markup contains a false price and rating.
After: Markup matches the actual visible offer and excludes unsupported claims.
Notes stay in this browser. Download a copy to keep them.
Recheck after price or inventory changes; validation does not guarantee an enhanced result.
Before you move on
A retailer’s product markup passes a syntax test, but its price is outdated and a manager promises guaranteed rich results after deployment. Which response best corrects both the implementation problem and the expectation about what structured data can deliver?
Next lesson →Google: Structured data introduction
Google: Structured data guidelines
Google: Optimizing for generative AI search
Lesson edition 2 · References reviewed 14 October 2026 · Practical exercise reviewed 15 October 2026. Examples are illustrative teaching scenarios.
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