Fine-tuning
Dated sources are over 18 months old; other dates are unknown.
Publication dates and source age
Sources counted: 2
Newest dated source: 2024-07
Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.
Some publication dates are unknown; the newest dated source may not be the newest source overall.
Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.
Open in the glossary Reading notes · Structured record
In plain language
Additional training that adapts an existing model using examples chosen for a task. [1]
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Limits & distinctions
Putting examples into a prompt leaves the model's weights unchanged. Fine-tuning changes learned settings and needs its own evaluation. [1] [2]
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A fuller explanation
Fine-tuning updates learned parameters. Some methods update all of them; others train only a smaller set. For example, training could use examples of how to categorize incoming support messages. [1]
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How it relates to the map
Ask what examples and goals were used, and how the adapted model was evaluated. [1] [2]
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https://theaiatlas.org/ideas/fine-tuning/
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Sources and what we read
1. LLMs: Fine-tuning, distillation, and prompt engineering
Publication dates and source age
Sources counted: 1
Publication dates are unavailable.
Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.
Some publication dates are unknown; the newest dated source may not be the newest source overall.
Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.
Read fine-tuning and prompt engineering, including the distinction between parameter updates and examples supplied as input. Used to distinguish these processes, without adopting general claims that fine-tuning is always necessary or improves every task.
2. Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile
Source is over 18 months old.
Publication dates and source age
Sources counted: 1
Newest dated source: 2024-07
Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.
Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.
Read introduction, section 2.2 on confabulation, and selected MEASURE actions 2.3, 2.5, 2.6, 2.7 and 2.9 concerning evaluation evidence, generalization, citations, generated-code review and safeguards. A voluntary risk-management profile; no claim that all 64 pages or every referenced study was reviewed.
Edition and machine-readable evidence
Content version 0.20.0. Evidence cutoff 2026-09-15; this does not mean every source was read on that day.
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