Fine-tuning
Fine-tuning keeps training a ready-made model with carefully chosen examples from one task or field. This changes the model's learned weights and behavior.
Another example
An ML engineer reviews 18,000 support chats and splits them by customer before training. The tuned model improves on a separate test set without seeing its examples. Putting examples inside a prompt is not fine-tuning.
Main parts
- 01base checkpoint
- 02training examples
- 03held-out evaluation
Use it when
Use this term when selected task or field data is used to update a model's weights.
Do not use it when
Do not choose fine-tuning until you understand problems in the prompt, search, data quality, and tests.
Name used in code
pretrained weights + supervised examples → adapted checkpoint 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 Fine-tuning to make AI behavior measurable and tied to evidence. User context: An ML engineer reviews 18,000 support chats and splits them by customer before training. The tuned model improves on a separate test set without seeing its examples. Putting examples inside a prompt is not fine-tuning. AI method or concept: Fine-tuning. Why it fits: Use this term when selected task or field data is used to update a model's weights. Do not use it when: Do not choose fine-tuning until you understand problems in the prompt, search, data quality, and tests. 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 Fine-tuning. Definition: Fine-tuning keeps training a ready-made model with carefully chosen examples from one task or field. This changes the model's learned weights and behavior. 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.