Model Inference
Inference is when a trained model uses new input to make a prediction, give a score, or create an answer. Its learned weights do not change.
Another example
An engineer gives a deployed model a 240-token incident report. The model returns a severity label and a timing record. Changing the model through gradient updates is training, not inference.
Main parts
- 01trained weights
- 02runtime input
- 03prediction or generation
Use it when
Use this term for a model running in an app, including its capacity, delay, and output speed.
Do not use it when
Do not use it for training a model, planning a test, or describing how a person thinks.
Name used in code
model.forward(input) → output 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 Model Inference to make AI behavior measurable and tied to evidence. User context: An engineer gives a deployed model a 240-token incident report. The model returns a severity label and a timing record. Changing the model through gradient updates is training, not inference. AI method or concept: Model Inference. Why it fits: Use this term for a model running in an app, including its capacity, delay, and output speed. Do not use it when: Do not use it for training a model, planning a test, or describing how a person thinks. 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 Model Inference. Definition: Inference is when a trained model uses new input to make a prediction, give a score, or create an answer. Its learned weights do not change. 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.