Earlier approaches to language processing read text strictly in order, which made it hard to connect a pronoun to a noun several sentences back. The transformer introduced a mechanism called attention, which lets the model look at the whole input at once and decide which parts matter for interpreting each word. That is what allows a model to understand that in a paragraph about a boiler service, the word it in the last sentence refers to the boiler and not the engineer.
The other significant property is that transformers parallelise well. Because the model is not forced to work word by word in sequence, training can be spread across large amounts of hardware, which is what made models of the current scale practical to build in the first place.
You do not need to think about transformers to run a chat widget on your website, any more than you need to understand engine design to drive. What the architecture buys you is comprehension: a visitor can ask a question in their own clumsy phrasing and still get a sensible answer. Clerkzo builds on that to answer questions from your crawled site content and capture booking requests conversationally rather than forcing people through a rigid form.
Related terms
Browse all 142 terms- Document Object Model (DOM)The Document Object Model, or DOM, is the structured map a browser builds of your web page after loading it. Code can read and change that map, which is how anything on a page moves, updates or responds to a click.
- Model TemperatureModel temperature is a setting that controls how much randomness an AI model applies when choosing its next word. Lower values produce consistent, predictable output; higher values produce more varied and creative output.
- Model DriftModel drift is the gradual decline in an AI system's accuracy that happens when the real world moves away from the conditions it was built for. The model has not changed; the situation around it has.
- Large language model (LLM)A large language model (LLM) is an AI system trained on very large amounts of text to predict the next piece of language in a sequence. That single capability lets it answer questions, summarise documents, write code and hold a conversation.