RankBrain / DeepRank / FastSearch

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.

Learned mathematical representations (embeddings) of search queries and documents, plus behavioral calibration (which search results did users actually rate as satisfying?).

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]

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.

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).