BERT Algorithm

BERT, short for Bidirectional Encoder Representations from Transformers, is a breakthrough natural language processing (NLP) model developed by Google. Rolled out to Google Search in 2019, it marked a turning point in how search engines interpret human language—particularly conversational queries and complex, long-tail searches.

Unlike earlier systems that read words one after another, BERT analyzes language in both directions at the same time. By considering the words that appear before and after a given term, it captures context more accurately, enabling Google to distinguish subtle differences in meaning. This bidirectional understanding allows it to interpret connecting words—like prepositions and conjunctions—that often change the intent of a query.

BERT is especially effective for handling natural, conversational questions, queries with multiple concepts, and searches where small wording choices create significant differences.

For SEO and GEO strategies, BERT’s influence extends beyond search engines, shaping how AI systems process queries across multiple platforms. Content that is clear, contextual, and naturally written is more likely to align with how BERT and similar models interpret meaning.

To optimize for BERT, businesses should focus on content that flows naturally, directly answers user questions, and provides rich detail. Keyword stuffing or overly rigid phrasing is less effective than writing content that fully captures the intent behind a search.