Semantic Search and Embeddings, Explained for SEOs

Semantic search isn’t new, and neither is the anxiety about it. Google has been moving away from pure keyword matching toward understanding meaning for the better part of a decade, in stages that were each treated as revolutionary at the time. Understanding that history is actually the most useful way to think about what AI Overviews and AI-generated answers are doing now — they’re a continuation of the same underlying approach, not a break from it.

The three stages that got us here

RankBrain went live internally around April 2015 and was revealed publicly through a Bloomberg interview that October. Google’s Greg Corrado described it at the time as translating queries into mathematical vectors to help handle the roughly 15% of daily searches Google had never seen before — queries with no prior data to learn from. It was called the third most important ranking signal at the time, a striking claim for a system built specifically to handle novelty rather than precision matching.

BERT arrived next, announced via Google’s official blog in October 2019. Google called it “one of the biggest leaps forward in the history of Search,” and it initially affected roughly 10% of US English queries before expanding to more than 70 languages by December of that year. Where RankBrain worked on the query side, BERT was fundamentally about understanding context and nuance within language itself — the difference between “to” as a direction versus part of a phrase, the kind of subtlety that pure keyword matching has always struggled with.

MUM followed, announced at Google I/O in May 2021. Google described it as roughly 1,000 times more powerful than BERT, built on the T5 framework, and multimodal — capable of understanding across text and images together, not just text alone. Google confirmed MUM-based features began rolling into Search later that year.

RankBrain solved for queries with no precedent. BERT solved for language with genuine nuance. Each step was framed as revolutionary, and each one was really the same underlying idea — represent meaning as vectors, then match on meaning instead of exact words — applied to a progressively harder version of the problem.

What “vectors” actually means, without the math

The core idea across all three systems: instead of matching your search query’s literal words against the literal words on a page, the system converts both the query and the content into a mathematical representation — a vector — that captures meaning. Two pieces of text that mean similar things end up with similar vectors, even if they don’t share a single word in common. That’s how Google can match “best way to get rid of a headache fast” against a page titled “quick headache relief remedies” without those two phrases sharing much literal vocabulary.

Google has confirmed this same family of vector-embedding technology now powers Search, YouTube, and Google Play, and is available externally through Google Cloud’s BigQuery and Vertex AI vector search products — a hybrid approach combining dense and sparse embeddings reached general availability in 2025. The technology isn’t confined to the search results page anymore; it’s infrastructure Google offers to other developers building their own retrieval systems.

How this connects directly to AI Overviews

Google’s own developer documentation describes AI Overviews as built on retrieval-augmented generation — using Search’s existing core retrieval and ranking systems to fetch and “ground” real, live web content, which Gemini then synthesizes into a generated answer. That’s a meaningful confirmation: AI Overviews aren’t a separate system bolted onto Search. They depend on the same retrieval infrastructure that’s been evolving through RankBrain, BERT, and MUM — the generative layer is new, but the underlying “find content that means the same thing as this query” mechanism is not.

I want to be precise about the limits of that claim, though: Google has described the same underlying vector/embedding approach being reused across these systems. That’s different from claiming AI Overviews literally runs the exact same RankBrain or BERT models under the hood — Google hasn’t stated that direct a lineage, and I wouldn’t overstate it as more than “conceptually the same family of technique, evolved.”

Why this history actually matters for SEO practice

If you’ve internalized that Google has been matching on meaning rather than exact keywords since at least 2015, a lot of “how do I optimize for AI search” advice starts to look less like a new discipline and more like an intensified version of an old one:

• Semantic clarity has mattered for a decade already. Writing content that clearly and unambiguously expresses what it’s about — not stuffed with exact-match keyword variants — has been the better strategy since BERT, not since AI Overviews arrived.
• Comprehensive topical coverage beats keyword-targeted fragmentation. A vector-based system rewards content that thoroughly represents a topic’s meaning, which tends to favor genuinely comprehensive pages over a scattered set of thin pages each targeting one exact-match phrase.
• The retrieval step is still doing real work, generative layer or not. Since AI Overviews depend on the same retrieval and grounding infrastructure as regular Search, the fundamentals that get a page retrieved and ranked well in normal search results are the same fundamentals that make it eligible to be retrieved and cited in an AI-generated answer. There isn’t a separate, secret “AI optimization” ruleset running in parallel — it’s the same retrieval system, with a generation step added on top.

The through-line from RankBrain to AI Overviews is longer and more continuous than the AI-search hype cycle tends to suggest. Understanding it doesn’t just satisfy curiosity — it’s a genuinely useful antidote to the idea that AI search visibility requires an entirely new playbook, when a lot of it is the same semantic-relevance fundamentals that have been building for the better part of a decade.