An SEO glossary of terms every marketer should know in 2026
A practical, current glossary covering the essential SEO terminology every marketer should be comfortable with, from foundational concepts to 2026-specific terms.
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In October 2019, Google rolled out BERT (Bidirectional Encoder Representations from Transformers), which it described as one of the biggest leaps forward in search history, affecting roughly one in ten English-language queries at launch. BERT was not built to catch spam or reward better content directly — it was built to understand language itself more precisely, particularly the small connecting words that change a sentence's meaning entirely.
The problem BERT solved
Before BERT, Google's language models processed words largely in sequence, which meant short, common words like 'for', 'to' and 'not' were sometimes effectively ignored, since they carry little standalone meaning. But those words often carry the entire meaning of a sentence. 'A parking space for disabled' and 'a parking space, not disabled' mean opposite things, and older systems could genuinely struggle with that distinction.
- Prepositions that reverse or redirect meaning ('to', 'for', 'without').
- Negations that flip a statement's intent ('not', 'never').
- Comparative language ('better than', 'instead of') that older matching struggled to weigh correctly.
What 'bidirectional' actually means
BERT reads a sentence in both directions at once, considering the words before and after a given word simultaneously to understand its role in context, rather than processing strictly left to right. This is what allows it to correctly interpret a word like 'bank' differently in 'river bank' versus 'savings bank', based on everything else in the sentence.
BERT's real achievement was teaching Google to stop skimming past small words — and small words are often exactly where a sentence's real meaning lives.
What this meant for SEO content
BERT rewarded content that read naturally and precisely, and quietly punished content written in fragmented, keyword-stuffed phrases that ignored proper grammar. A page written as 'best shoes running women comfortable' communicates less precisely to a BERT-aware algorithm than a page written in complete, natural sentences covering the same topic.
- Write in complete, grammatically natural sentences rather than keyword fragments.
- Pay attention to qualifiers, comparisons and negations in your own content — precision helps BERT-era matching, not just readers.
- Answer nuanced, multi-part questions directly rather than only the broad topic.
BERT's place in the broader language model story
BERT sits between RankBrain (2015) and MUM (2021) in Google's progression toward genuine language understanding. Where RankBrain groups similar queries and MUM extends understanding across languages and content formats, BERT's specific contribution was grammatical precision within a single query and page. The original BERT research is documented publicly on Google's AI blog, and the model's architecture is explained on its Wikipedia page).
Frequently asked questions
Do I need to write differently to optimise specifically for BERT?
No specific technique targets BERT directly. Writing clearly, naturally and precisely — the way you would explain something to a person — is the entire optimisation, since that is exactly what BERT is built to understand well.
Is BERT still active in Google's ranking systems?
Yes, BERT remains part of how Google processes language in search, working alongside later models rather than being replaced by them.
Did BERT replace RankBrain?
No, the two work together. RankBrain groups conceptually similar queries; BERT improves the grammatical precision of understanding any single query, including novel ones RankBrain is handling.
If your existing content was written in the keyword-fragment style that predates BERT, a rewrite pass could be worth more than you'd expect. Talk to us about a content review.
The takeaway
Write content that answers the full nuance of a question, including comparisons and qualifiers — BERT's whole purpose was making sure Google no longer ignores the small words that change meaning.
Written by Fibvo — Bengaluru and Erode, India — working with clients in other time zones. Questions, or think we have it wrong? Tell us.
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