Knowledge base v2.13.0 · Community edition · data through 2 October 2026
Day 3 · Friday 2 October 2026 · 13:25
Search features, rich results and structured data. Speaker from the next speaker's reference on stage to Gary's product rich result example; the same person as the retrieval part of Making sense of users' queries.
Google's serving diagram shows the query passing through query understanding and retrieval to the index, then back through ranking and search features to the user, with the Search Features step highlighted (example query 'Where to eat orange').
Speaker Gary IllyesEvidence slide photo
Google's rich results slide said Article structured data is not required to appear in Top Stories but is highly recommended for all articles.
“Not required for "Top Stories," but highly recommended for all articles.”
Wording checked against the slide or recording
Speaker Gary IllyesEvidence slide photo, transcript
Used byrequirement DEV-SDA-13
Google's Article rich results slide said structured data can be used to indicate that content is behind a paywall.
Speaker Gary IllyesEvidence slide photo
Used byrequirement DEV-SDA-13
Google's Article slide showed the NewsArticle JSON-LD example from Google's Article documentation: a block in the page head with headline, three image URLs (1x1, 4x3 and 16x9), datePublished and dateModified with time zone offsets, and an author list of Person objects with name and url.
Speaker Gary IllyesEvidence slide photo
Used byrequirement DEV-SDA-13
Google described its search results page as an auction system in which the individual results, of different types, all bid for a place on the page; the auction is a metaphor for result types competing for space, not a reference to ads.
Speaker Gary IllyesEvidence transcript
Text results are the most common and most prominent result type on Google's results page, whether they are generated by an LLM or not, or enhanced in some other way, Google's speaker said.
Speaker Gary IllyesEvidence transcript
Which kinds of results Google shows for a query is decided by query understanding, which tries to predict the intent behind the query.
Speaker Gary IllyesEvidence transcript
When the query itself names the wanted result type, as 'pictures' does in 'orange pictures' (or a query asking for videos or news), Google can read the intent directly from that word.
Speaker Gary IllyesEvidence transcript
Google said it shows images on the web results page, not only in image search, because from 2005-2006 it saw people appending words such as 'pictures' or 'images' to their queries, which led to the launch of universal search.
Speaker Gary IllyesEvidence transcript
Used byglossary term Universal search
For an ambiguous query such as 'orange', Google uses historical data: it looks at how users reacted to different kinds of results for previous and similar queries and shows the result types users prefer.
Speaker Gary IllyesEvidence transcript
Google handles an image used as a search query much like a text query interpreted as an embedding: the image is broken down into vectors (embeddings) that are then searched for in the index.
Speaker Gary IllyesEvidence transcript
Google can still detect intent for image queries, even though they are searched as embeddings, the speaker said.
Speaker Gary IllyesEvidence transcript
Google uses an abstract drawing of the search results page in its documentation so that the illustration does not go out of date as fast as a screenshot.
Speaker Gary IllyesEvidence transcript
A text result on Google's results page is made of parts such as the attribution, the title link, the snippet and the site name.
Speaker Gary IllyesEvidence transcript
Google generates the parts of a text result, such as title link and snippet, from its understanding of the underlying web page, even when the site owner provides nothing extra.
Speaker Gary IllyesEvidence transcript
Used byrequirement DEV-HTM-03
Google can add elements to a text result, such as a 'highly cited' badge or a preferred-source badge.
Speaker Gary IllyesEvidence transcript
Some search features work the other way round: a feature container holds text results inside it; the speaker could not recall that feature's name and suggested, unsure, linked subscriptions.
Speaker Gary IllyesEvidence transcript
Image results shown among web results come from Google's image index and are roughly the same images that Google Images shows for the same query.
Speaker Gary IllyesEvidence transcript
Video results in web search have thumbnails and attribution that come from the page or from its markup.
Speaker Gary IllyesEvidence transcript
Google generates search features as the last serving step: it sends the query to the index, gets results back, ranks them and then builds the search features.
Speaker Gary IllyesEvidence transcript, slide photo
Used byglossary term Search features
Most of Google's search features need nothing extra from the site owner; Google generates them from what it extracted from the page during indexing.
Speaker Gary IllyesEvidence transcript
Used byglossary term Search features
Rich results differ from other search features because Google builds them from extra data that site owners provide, usually structured data and usually in JSON-LD format.
“The magic behind rich results is usually structured data. Emphasis on "usually".”
Speaker Gary IllyesEvidence transcript
Used byglossary terms Rich results, Search features
AI Mode and AI Overviews are not rich results but standard search features: they need no structured data to function and work with the normal text results from Google's index.
“AI Mode and AI Overviews are not rich results.”
Speaker Gary IllyesEvidence transcript
Used byrequirement DEV-AIF-02glossary terms Rich results, Search features
Google said that if AI Mode one day showcases images, recipes or similar elements, those parts would need structured data, but in their current form AI Mode and AI Overviews require pretty much nothing extra.
Speaker Gary IllyesEvidence transcript
Google's structured data feature guide lists the kinds of structured data Google supports with a search feature and what each can do to a site's search results.
Speaker Gary IllyesEvidence transcript
Article structured data is a broad type with many properties and nested types, which Google uses to learn more about published content, such as its headline, author and publication date.
Speaker Gary IllyesEvidence transcript
Used byrequirement DEV-SDA-13
Google described the site name as an attribution feature rather than a rich result, and said site owners can influence which site name Google shows for their site in search results.
Speaker Gary IllyesEvidence transcript
Used byrequirement DEV-SDA-15glossary term Site name
Review structured data lets a site specify how its users rated something; the review snippet shows an average star rating and often the number of reviews of a product, service or piece of content.
Speaker Gary IllyesEvidence transcript
Used byrequirement DEV-SDA-14glossary term Review snippet
Google said users rely quite a bit on review stars, calling them a powerful signal of quality and trust for users and a way for sites to use social proof to increase clicks.
“Those little yellow stars are a powerful signal of quality and trust for our users.”
Speaker Gary IllyesEvidence transcript
Review snippets can be used for many kinds of things, from products to local businesses, movies and books.
Speaker Gary IllyesEvidence transcript
Used byrequirement DEV-SDA-14
Some countries rely a lot on social proof such as reviews, Google's speaker said, recalling a point from Google's Day 2 internationalisation talk.
Speaker Gary IllyesEvidence transcript
Google called product rich results essential for any e-commerce business; they can show the product's rating, the number of reviews and other details.
Speaker Gary IllyesEvidence transcript
Google Search displays product rich results from product structured data; it may also use the product data a merchant provides in Google Merchant Center, but Merchant Center is not required for them.
Speaker Gary IllyesEvidence transcript
Used byrequirement DEV-SHP-01glossary term Merchant Center
Product annotations on Google Images results also come from product structured data and do not need Google Merchant Center.
Speaker Gary IllyesEvidence transcript
Used byrequirement DEV-SHP-01
To appear in the Shopping tab of Google Search, a product must be submitted in Google Merchant Center; product structured data alone is not enough.
Speaker Gary IllyesEvidence transcript
Used byrequirement DEV-SHP-01glossary term Merchant Center
Product structured data can help Merchant Center in some cases, for example during data validation, but Merchant Center does not require it.
Speaker Gary IllyesEvidence transcript
Used byrequirement DEV-SHP-01
Google launched universal search on 16 May 2007, blending results from Images, Maps, Books, Video and News into its web results.
Publisher Official Google Blog (16 May 2007)
Used byglossary term Universal search
Google's How Search Works pages say Google uses aggregated and anonymized interaction data to assess whether search results are relevant to queries, turning that data into signals for its machine-learned systems.
Publisher Google Search (How Search Works)
Google's Visual Elements gallery describes a text result as based on the page's textual content, with attribution, title link and snippet; attribution can include the site name, favicon and visible URL, and a rich result typically relies on structured data.
Publisher Google Search Central
Google's AI features guide says there are no additional requirements, and no special schema.org structured data, needed to appear in AI Overviews or AI Mode; a page must be indexed and eligible to be shown with a snippet.
Publisher Google Search Central
Used byrequirement DEV-AIF-02
Google's Article documentation says there is no markup requirement to be eligible for Google News features like Top stories, and that Article markup can tell Google more explicitly that content is a news article, who the author is and what the title is.
Publisher Google Search Central
Used byrequirement DEV-SDA-13
Google's site name documentation says site names are generated automatically from a site's home page and references to it on the web, and that a site can indicate its preferred name with WebSite structured data on its home page.
Publisher Google Search Central
Used byrequirement DEV-SDA-15glossary term Site name
Google's review snippet documentation supports ratings for local businesses and organizations only on sites that capture reviews about other businesses; self-serving reviews of a site's own business are not eligible.
Publisher Google Search Central
Used byrequirement DEV-SDA-14glossary term Review snippet
Google's Product structured data introduction says that providing both on-page structured data and a Merchant Center feed maximizes eligibility for shopping experiences and helps Google understand and verify the data; product snippets may take pricing from the feed when the markup lacks it.
Publisher Google Search Central
Used byrequirement DEV-SHP-01
Google's Merchant Center help says products and details crawled from an online store can show up on Google even when they are not marked up with schema.org or added in Merchant Center, and lists the Shopping tab among the places Merchant Center free listings appear.
Publisher Google Merchant Center Help
Merchant Center's automatic item updates use structured data and other product data found on landing pages to fix price, sale price, availability and condition mismatches in a merchant's product data; Google says they do not replace regular feed updates.
Publisher Google Merchant Center Help
Used byrequirement DEV-SHP-01
Google's 2007 launch post explained universal search by the growing number of separate search tools and traced it to a 2001 results-page mockup; the 2005-2006 image-seeking queries named on stage are a further motivation the post does not mention, and they predate the May 2007 launch.
Author Ibrahim Anjro
Day 2 said the structured data Google processes also feeds AI Overviews and AI Mode (D2-C476), while Day 3 said these features do not need structured data to function: markup is not a condition for appearing in AI features, but data taken from it can still reach them.
Author Ibrahim Anjro
Used byrequirement DEV-AIF-02
Google's free listings help says crawled store products can appear on Google without markup or Merchant Center but does not say whether that includes the Shopping tab; for a shop that wants Shopping tab visibility, the stage rule that Merchant Center is required is the safe working assumption.
Author Ibrahim Anjro
For an online shop, Product structured data is enough for product rich results and Google Images product annotations, but the Shopping tab needs a Merchant Center feed too; keep feed and markup values identical, since Google can use the markup to validate the feed.
Author Ibrahim Anjro
Used byrequirement DEV-SHP-01
Which kinds of results Google shows for a query is decided by query understanding, which tries to predict the intent behind the query.
Google placed query understanding in the traditional part of Search, the part where answers are looked up in an index and then presented and ranked.
Google handles an image used as a search query much like a text query interpreted as an embedding: the image is broken down into vectors (embeddings) that are then searched for in the index.
Besides posting lists, Google can retrieve documents through vector embeddings: parts of documents are associated with embeddings, which form a vector space used for retrieval.
Google generates the parts of a text result, such as title link and snippet, from its understanding of the underlying web page, even when the site owner provides nothing extra.
The snippet shown for a web result is reconstructed from the tokens stored in Google's index: Google knows the position of each token in the document and rebuilds the snippet from those positions.
Google can add elements to a text result, such as a 'highly cited' badge or a preferred-source badge.
Sites that users mark as a preferred source are more visible in Top Stories and are labelled in AI Mode and AI Overviews.
Image results shown among web results come from Google's image index and are roughly the same images that Google Images shows for the same query.
An image Google has extracted can appear almost anywhere Google shows results, including Discover, image search, web search and AI features, so its potential reach is immense.
Most of Google's search features need nothing extra from the site owner; Google generates them from what it extracted from the page during indexing.
The 'gold nuggets' that Google's feature extraction step pulls out of a page's HTML are structured data (such as JSON-LD), images and videos.
Rich results differ from other search features because Google builds them from extra data that site owners provide, usually structured data and usually in JSON-LD format.
Structured data makes pages eligible to appear as rich results, Google's structured data talk said in its recap.
AI Mode and AI Overviews are not rich results but standard search features: they need no structured data to function and work with the normal text results from Google's index.
AI Overviews and AI Mode use the same index structures and token-based snippets as classic web results, a point Google called important but not obvious.
Review structured data lets a site specify how its users rated something; the review snippet shows an average star rating and often the number of reviews of a product, service or piece of content.
Structured data turns the loosely structured web into structured information that powers visual search features such as review stars and recipe filters (for example by preparation time).
Some countries rely a lot on social proof such as reviews, Google's speaker said, recalling a point from Google's Day 2 internationalisation talk.
According to consumer data shown on a slide in the talk (source not captured), US consumers judge product quality more by user feedback and reviews, while European shoppers seem to look more at brand reputation.
Google handles an image used as a search query much like a text query interpreted as an embedding: the image is broken down into vectors (embeddings) that are then searched for in the index.
For visual search, Google breaks an image down into vectors, sends the vectors to the index and returns results based on them.
Google's serving diagram shows the query passing through query understanding and retrieval to the index, then back through ranking and search features to the user, with the Search Features step highlighted (example query 'Where to eat orange').
Google's opening slide for serving day showed serving as the third stage after crawling and indexing, and drew Google's serving infrastructure as query understanding and retrieval leading into the index, then ranking and search features leading back to the user, for the example query 'Where to eat jamon'.
Google's structured data feature guide lists the kinds of structured data Google supports with a search feature and what each can do to a site's search results.
Google recommends using the Search gallery in its developer documentation to find the structured data features that suit a site; the gallery shows each feature and how Google uses the markup.