Transformer Architecture
The transformer architecture is the neural network model presented by Google researchers in 2017 on which practically all modern large language models are based. Its central innovation was to dispense with sequential processing altogether and to determine solely through self-attention how strongly each word in a text relates to every other; the attention mechanism itself is older than the transformer architecture.
In practice
For companies the transformer architecture is not a tool to be used directly but technical background knowledge. It explains why modern language models handle far longer stretches of text correctly than earlier AI text systems, and why they can connect references across many sentences within long documents.