Transformer
Definition
Transformers process information by weighting relationships between parts of an input.
A sequence model architecture that processes representations mainly with attention and feedforward blocks instead of recurrence.
Simply explained
When reading, the model looks at which text parts are important to each other. This way, it connects information from different parts of the text.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
A company uses an encoder transformer for classification of technical tickets.
Limits and pitfalls
- Transformer does not guarantee long context usage or factual accuracy; computational requirements, data, and architecture variant are decisive.
Origin & history
Vaswani and coauthors published the Transformer in 2017 for machine translation.
Use in mid-sized companies
- Possible example: A company uses an encoder transformer for classification of technical tickets.
Frequently asked questions
What does Transformer mean?
A sequence model architecture that processes representations mainly with attention and feedforward blocks instead of recurrence. What should one pay attention to? Transformer does not guarantee long context usage or factual accuracy; computational requirements, data, and architecture variant are decisive.
Sources
- Attention Is All You Need · Vaswani et al., Attention Is All You Need
Source checked on 10.10.2026. Editorial usage examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: