RankBrain / DeepRank / FastSearch
These systems measure the semantic fit between a search query and a page — whether the meaning matches, not just the keywords.
What it measures
These systems measure the semantic fit between a search query and a page — whether the meaning matches, not just the keywords. RankBrain was Google's first AI system for this purpose; DeepRank is a deeper language understanding layer; FastSearch accelerates this estimate for faster responses. The key point: these systems measure relevance, not content quality.
Derived from
Learned mathematical representations (embeddings) of search queries and documents, plus behavioral calibration (which search results did users actually rate as satisfying?).
Metric detail
RankBrain = an ML system for (novel) queries via embeddings; DeepRank = deeper language understanding (BERT-related); RankEmbedBERT = the BERT-based embedding field. They measure fit, not quality. [B fields, O RankBrain/BERT]
Why this attribution
Expertise, Authority and Trust are each indirect — these systems flank all E-E-A-T dimensions through better meaning understanding, but don't measure them directly. Experience has no bearing: meaning similarity doesn't touch first-hand material.
Strategic consequence
Keyword stuffing (inserting as many search terms as possible) no longer works — RankBrain recognizes whether the meaning fits. Writing text that genuinely answers the information need behind a query performs better. RankBrain is also a bridge to AI citation: pages that are semantically well positioned have better chances of appearing in AI Overviews (AIO).