AI search
Embedding
An embedding is a numeric representation of text that places similar meanings close together in a high-dimensional space.
What is embedding?
An embedding is a numeric representation of text that places similar meanings close together in a high-dimensional space. Retrieval systems compare the embedding of a query against the embeddings of stored passages to find the ones that match in meaning.
Why it comes up
It is why a passage can be retrieved for a query that shares no words with it, and why exact-match keyword thinking understates what a page can rank for.
In practice
Generated by a model and stored in a vector index. Similarity is usually measured as cosine distance, and the retrieval quality depends heavily on how the text was chunked before embedding.
What it is not
Semantic similarity is not the same as correctness. A passage can be retrieved because it is topically close while being the wrong answer.
Worth knowing because it comes up in almost every conversation about this.
Where this comes up in the work
It is part of Local SEO, and more specifically of Local links and mentions. Across the rest of the work it shows up in:
- Content and Editorial, since deciding how long a piece needs to be from what it has to prove is only as good as this is
- Lifecycle and Email: it is the reason keeping the sending domain healthy before scale makes it urgent is scoped the way it is
- Analytics and Measurement, because testing with a holdout where the spend justifies proving causation depends on it
- Brand and Creative, where it quietly limits how far measuring branded search instead of reporting impressions can go
What to check
Two questions that establish whether this is actually in order on a given site:
- Whether it survives a logged-out visit from a cold cache
- Whether the exception somebody added two years ago is still needed
Common questions
What tells you whether embedding is in order?
Generated by a model and stored in a vector index. Similarity is usually measured as cosine distance, and the retrieval quality depends heavily on how the text was chunked before embedding. Anybody with access can establish it in an afternoon. What does a technical SEO audit actually check? handles the common case; What is a good conversion rate? handles the one that catches people out.
Who owns embedding in a business?
Whoever is accountable for adapting the experience only where it demonstrably helps. Problems with this tend to persist because nobody is. Thin content if the constraint is budget, Topical authority if the constraint is time.
How much work is embedding?
Less than people expect to check and sometimes more than they expect to fix. Generated by a model and stored in a vector index. Answer Engine Optimization for Nonprofit and NGO goes through this properly, and App Store Optimization for Nonprofit and NGO shows where it stops working.
How urgent is embedding?
It comes before work that assumes it is already handled, and after anything actively blocking indexation.
How would we know embedding is a problem?
By measuring it rather than by argument. Generated by a model and stored in a vector index.
Is embedding just another name for something?
Semantic similarity is not the same as correctness. A passage can be retrieved because it is topically close while being the wrong answer. Technical SEO in Ajmer is the longer treatment. Website Design and Development in Ajmer is the one to send to whoever signs off.
Ask what we would do first, and why
No pitch deck. A short call, a look at the data you already have, and a view on whether this is worth doing at all.