What Is the Transformer Architecture? The Foundation of Modern AI Search

The transformer is a neural network architecture introduced in 2017 that processes all tokens in a sequence simultaneously using a mechanism called self-attention, rather than processing them sequentially like earlier models.

Self-Attention

Self-attention allows each token to look at every other token in the input and decide how much attention to give each one. This enables the model to understand context and resolve ambiguity.

Encoders vs Decoders

BERT uses the encoder (understanding and classification). GPT uses the decoder (text generation). Google uses BERT-style encoders for search ranking and understanding.

SEO Implications

• Google uses transformer models to understand query intent and content meaning
• Context and topical depth matter more than exact keyword matching
• Clear, well-structured content helps transformer models extract meaning accurately