Glossary
Transformer Model
A transformer is a neural-network architecture that processes relationships among tokens using attention mechanisms. Introduced in 2017, it underpins modern large language models and allows models to weigh relevant parts of a sequence when producing or interpreting text.
In plain terms
It is the core architecture behind modern language models.
Why it matters
Understanding transformers clarifies that an LLM predicts language from patterns; search and grounding are additional systems layered around it.
How to apply it
- Separate model behavior from retrieval behavior in analysis.
- Avoid claiming that content changes retrain public models.
- Focus optimization on accessible evidence and clear meaning.
Example
An AI search product combines a transformer language model with a separate search index that supplies current source passages.
Sources
Related reading
Related terms
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