Concept dictionary
Fine-Tuning
Fine-tuning is additional training applied to an existing model using selected examples or feedback to change its behavior for a task, domain, or response pattern.
- Review state
- Reviewed in POC
- Freshness
- Current
- Version
- 2
Also called
- Fine-Tuning
- Fine Tuning
Remember it this wayFine-tuning changes the model; retrieval changes what the model can consult now.
In plain language
Instead of building a model from the beginning, a team starts with a pretrained model and adjusts some of its parameters using a narrower training process.
Why it matters
Fine-tuning can improve consistent behavior, but it creates data, evaluation, maintenance, cost, and version-management obligations that prompting or retrieval may avoid.
Example
A company fine-tunes a model on reviewed examples so it follows a specialized output format more consistently.
Common misconception
Fine-tuning is not the same as giving a model current documents to consult. Retrieval supplies information at use time; fine-tuning changes learned behavior.
Related terms
Sources
- NIST AI 100-2 E2025, Adversarial Machine LearningNIST terminology for model training and fine-tuning.
- Google for Developers, Machine Learning Glossarytechnical definition and examples of fine-tuning.
Learn more
- Compare with [[retrieval-augmented-generation|Retrieval-Augmented Generation]] before choosing an adaptation method.