Transformer
A Transformer is a kind of neural network. It uses attention to learn how parts of a sequence relate and can work on many positions at once.
Another example
An ML engineer sends 512 tokenized support messages into a model. Attention builds a view of the context at each position. Transformer names the architecture, not every product that uses a large language model (LLM).
Main parts
- 01token representations
- 02multi-head attention
- 03feed-forward layers
Use it when
Use this term when you explain an attention-based model, how it trains, or how it handles a sequence.
Do not use it when
Do not use it as another name for a chatbot, a language model, or any AI feature.
Name used in code
self-attention → feed-forward → residual Before you ship
Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback.
Outcome: Use or evaluate Transformer to make AI behavior measurable and tied to evidence. User context: An ML engineer sends 512 tokenized support messages into a model. Attention builds a view of the context at each position. Transformer names the architecture, not every product that uses a large language model (LLM). AI method or concept: Transformer. Why it fits: Use this term when you explain an attention-based model, how it trains, or how it handles a sequence. Do not use it when: Do not use it as another name for a chatbot, a language model, or any AI feature. AI requirements: Define the input and source evidence. Set model and tool permissions. Use versioned evaluation data and measures. Define failure, decline-to-answer, privacy, speed, and cost limits. Operational safeguards: Keep a clear trace and hide sensitive log data. Show users a safe fallback. Mark steps that need human review. Define how to roll back the model or prompt. Acceptance criteria: Record baseline and target measures on a versioned evaluation set. Test edge cases and hostile inputs. Verify fallback, monitoring, permissions, and rollback. Evidence and limits (evidence boundary): No single official standard controls the whole term. Its meaning may change across teams, platforms, or frameworks. Unknowns to confirm: Target task, model and version, evaluation owner, source data, risk limit, tool permissions, and production fallback.
Check this request
Review the current use of Transformer. Definition: A Transformer is a kind of neural network. It uses attention to learn how parts of a sequence relate and can work on many positions at once. Release checks: Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback. Before changing code, report the evidence you found, gaps, severity, and the smallest safe fix.