Fine-tuning
AIGP glossary · Last reviewed: · By Victor Humenhuk (AIGP certified)
Fine-tuning - Further training a pre-trained model on a smaller, task/domain-specific dataset.
In the AIGP body of knowledge, Fine-tuning comes up under Module 6: Governing AI Development · BoK III.A + III.B; Module 7: Governing AI Deployment · BoK IV.A; Module 8: AI Governance Vocabulary.
Fine-tuning in context
- Generative deployments add their own questions - fine-tuning, ==retrieval-augmented generation==, vector/graph databases and agentic architectures. (GenAI choices and the pre-launch checklist)
- The named techniques and model types that show up as one-line scenario answers - including ==federated learning== (data never leaves the site), transfer learning, fine-tuning, and the discriminative-vs-generative split. (Learning techniques and methods)
Where Fine-tuning is covered in the AIGP study notes
- Building, Training and the Three Lines of Defence
- GenAI choices and the pre-launch checklist
- Learning techniques and methods
Related terms
- Three lines of defence (3LOD)
- Effective challenge principle
- Retrieval-augmented generation (RAG)
- TEVV
- Active learning
- Adaptive learning
- Transfer learning model
- Federated learning
- Classification model
- Clustering
Test yourself on Fine-tuning
Recognising a definition is not the same as applying it in an exam scenario. Work through the free AIGP practice questions, or read the full AIGP study guide - every study note is free.