Knowledge base v2.13.0 · Community edition · data through 2 October 2026
Topic · Serving and ranking
After query understanding, Google looks up the expanded query in the index, which stores posting lists: for each word, the URLs associated with it. A document must contain the query's words, or related concepts in the case of embeddings, to be retrieved, and because a query can match millions of pages Google already orders the candidates at retrieval using per-document signals from indexing: language, then country, then quality. Google called quality the most important of these retrieval signals and said the same signal is used again in ranking (said at the event; Google's docs list quality among ranking signals but do not describe it as deciding retrieval). Author’s view: a page judged low in quality may never reach ranking, so quality is a precondition, not a final tweak. This builds on Day 2, which described the posting lists, retrieval through vector embeddings and the per-document signals, quality, country and language among them, attached to each document in the index. Gary Illyes said on Day 1 that for visual search Google breaks an image into vectors and retrieves results from the index with them, and on Day 2 that descriptive text around a video helps Google rank and retrieve it (both said at the event).
What to do
For visual search, Google breaks an image down into vectors, sends the vectors to the index and returns results based on them.
Speaker Gary IllyesIn Day 1, 11:45 · How Search works and where's AI?Evidence transcript
Descriptive text around a video helps Google rank and retrieve the video.
Speaker Gary IllyesIn Day 2, 13:50 · Using images to your advantage and Engaging Search users with videosEvidence transcript
Used byrequirement DEV-VID-02
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'.
Speaker GoogleIn Day 3, 10:15 · Welcome to serving and ranking day!Evidence slide photo
After query understanding and expansion, Google sends the queries to the index to look up pages, the retrieval step.
Speaker John MuellerIn Day 3, 10:25 · Making sense of users' queriesEvidence transcript
Used byglossary term Retrieval
Google's index uses posting lists: for each word, a list of the URLs associated with that word.
Speaker Gary IllyesIn Day 3, 10:25 · Making sense of users' queriesEvidence transcript
At retrieval, Google splits the query into words, applies query understanding and expansion, and matches the words and their expansions against the posting lists.
Speaker Gary IllyesIn Day 3, 10:25 · Making sense of users' queriesEvidence transcript
Used byglossary term Retrieval
For retrieval, Google uses signals attached individually to each document in the index.
Speaker Gary IllyesIn Day 3, 10:25 · Making sense of users' queriesEvidence transcript
The first condition for retrieving a document is that the query's words, or its concepts in the case of vectors or embeddings, are in the document or related to it.
Speaker Gary IllyesIn Day 3, 10:25 · Making sense of users' queriesEvidence transcript
Because a query like [best fried chicken ever] can match millions of pages, Google already orders the candidates during retrieval, before ranking starts.
Speaker Gary IllyesIn Day 3, 10:25 · Making sense of users' queriesEvidence transcript
To order candidates at retrieval, Google uses signals collected during indexing, and the first two are language and country.
Speaker Gary IllyesIn Day 3, 10:25 · Making sense of users' queriesEvidence transcript
Used byrequirement DEV-INT-07glossary term Retrieval
At retrieval, Google tries to match results to the user's language wherever possible: someone searching in Spanish does not necessarily want results in Italian.
Speaker Gary IllyesIn Day 3, 10:25 · Making sense of users' queriesEvidence transcript
Used byrequirement DEV-INT-07
Country is the second retrieval signal: a user searching from Switzerland wants cheese from Switzerland, not from Germany, and a user in Spain is poorly served by results targeting a South American country.
Speaker Gary IllyesIn Day 3, 10:25 · Making sense of users' queriesEvidence transcript
Used byrequirement DEV-INT-07
Google called quality the most important of the signals used to order candidates at retrieval: a URL of high quality is more likely to be retrieved from the index for specific queries.
“if the quality of a URL is high, then it's more likely to be retrieved from the index for specific queries.”
Speaker Gary IllyesIn Day 3, 10:25 · Making sense of users' queriesEvidence transcript
Used byglossary term Retrieval
The same quality signal that Google uses at retrieval is also used in ranking when results are served.
Speaker Gary IllyesIn Day 3, 10:25 · Making sense of users' queriesEvidence transcript
Quality acts twice: a page Google judges low in quality is less likely to be retrieved as a candidate at all, so it may never reach ranking, which makes content quality a precondition rather than a final tweak.
Author Ibrahim AnjroAnnotates Day 3, 10:25 · Making sense of users' queries
The retrieval talk went further than Google's documentation, which says Search returns the highest-quality and most relevant results and lists quality among key ranking signals, but never describes quality as deciding which pages are retrieved from the index at all.
Author Ibrahim AnjroAnnotates Day 3, 10:25 · Making sense of users' queries
At retrieval, Google splits the query into words, applies query understanding and expansion, and matches the words and their expansions against the posting lists.
In posting-list retrieval, the posting lists of the query's words are intersected, which yields an unranked list of candidate URLs.
At retrieval, Google splits the query into words, applies query understanding and expansion, and matches the words and their expansions against the posting lists.
At retrieval, Google looks up the posting lists of the query words that are actually important rather than of every word in the query.
The first condition for retrieving a document is that the query's words, or its concepts in the case of vectors or embeddings, are in the document or related to it.
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.
To order candidates at retrieval, Google uses signals collected during indexing, and the first two are language and country.
Country and language are among Google's most important signals and have been used since Google's early days.
To order candidates at retrieval, Google uses signals collected during indexing, and the first two are language and country.
Each document in Google's index has pretty much all the signals calculated for it attached, for example quality signals plus the page's country and language, according to an illustration the speaker called an approximation of the real structure.
At retrieval, Google tries to match results to the user's language wherever possible: someone searching in Spanish does not necessarily want results in Italian.
In ranking, country and language signals help Google serve users the right content for their country and language.
Country is the second retrieval signal: a user searching from Switzerland wants cheese from Switzerland, not from Germany, and a user in Spain is poorly served by results targeting a South American country.
Country and language are among Google's most important signals and have been used since Google's early days.
Google called quality the most important of the signals used to order candidates at retrieval: a URL of high quality is more likely to be retrieved from the index for specific queries.
Each document in Google's index has pretty much all the signals calculated for it attached, for example quality signals plus the page's country and language, according to an illustration the speaker called an approximation of the real structure.
Google's quality talk said quality is one of the most important ranking signals, although Google uses hundreds of ranking signals (the word 'quality' is a repaired speech-to-text reading).
The same quality signal that Google uses at retrieval is also used in ranking when results are served.
Google's index uses posting lists: for each word, a list of the URLs associated with that word.
For most of the tokens Google finds on the web, though not every single one, the index keeps a posting list of the URLs that contain that token.
For retrieval, Google uses signals attached individually to each document in the index.
Each document in Google's index has pretty much all the signals calculated for it attached, for example quality signals plus the page's country and language, according to an illustration the speaker called an approximation of the real structure.
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'.
Make each page's language unambiguous by translating the template with the main content
Rests on 12 claims, 3 of them in this topic
Give each important video its own watch page
Rests on 6 claims, 1 of them in this topic