Fine-Tuning
Definition
Fine tuning adapts an existing model with additional sample data.
Subsequently adapting an existing model with specific data sets to specialize in specialist domains, tasks or brand tones.
Simply explained
How to readjust an already trained tool for a special task.
Why it matters in practice
Helps to realistically classify the possibilities, quality and limitations of AI systems.
Example from business
Possible example: A company checks fine tuning for a recurring, easily measurable classification format.
Limits and pitfalls
- Use fine tuning, although better data, retrieval or a clearer prompt would suffice.
Origin & history
The Transformers documentation describes fine tuning as further training of a pre-trained model on task-specific data. The approach is to be distinguished from the provision of examples in the prompt.
Use in mid-sized companies
- Possible application in SMEs: A company checks fine tuning for a recurring, easily measurable classification format.
Frequently asked questions
What does fine-tuning mean?
Fine tuning adapts an existing model with additional sample data.
What errors should be avoided?
Use fine tuning, although better data, retrieval or a clearer prompt would suffice.
Sources
- Transformers: Fine-tuning · Hugging Face, Transformers: Fine-tuning
Sources checked on 09.10.2026. Editorial use cases are hypothetical; no evidence of real customer results.
Editorially reviewed on: