Glossary

Fine-tuning

What is Fine-tuning?

Fine-tuning means further training an already pre-trained AI model on additional, usually task-specific data. Its internal weights are adjusted so that the model handles, for instance, a particular writing style, specialist vocabulary or output format more reliably. Fine-tuning requires carefully prepared example data, computing power and testing. For many applications, well-written instructions and connected knowledge are sufficient and easier to change.

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Topics

Model training
Customisation
Language models

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Related terms

Parameter-efficient fine-tuning (PEFT)

Parameter-efficient fine-tuning (PEFT) covers techniques that adapt a large pre-trained model to a task by training only a small portion of its parameters or adding a few extra ones. Well-known methods include LoRA, QLoRA, adapters and prefix tuning. This greatly reduces computing and memory requirements, and small add-on modules can be stored for several tasks instead of full model copies. Example: a language model is adapted to the style of internal technical documents.

fine-tuning
training
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Instruction Tuning

Instruction tuning is a form of fine-tuning in which a pre-trained language model is trained further on many pairs of instructions and desired responses. This teaches the model to follow tasks phrased in natural language rather than merely continue text. It is a key step in turning a base model into an assistant and is often combined with methods such as RLHF. Example: 'Summarise this text in three sentences' is carried out reliably.

Fine-tuning
Language models
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Transfer Learning

In transfer learning, a model that has already learnt a large general task serves as the starting point for a new, specialised one. Instead of starting from scratch, it is adapted, for instance through fine-tuning, using comparatively little data of your own. This saves computing time and data, for example when a general image model is meant to learn to spot production defects in photos.

Machine learning
Training
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Retrieval-augmented generation (RAG)

Retrieval-augmented generation combines search with text generation: before answering, the system searches a knowledge collection – such as manuals, policies or FAQs – for relevant passages and passes them to the language model as context. The answer is therefore based on current, company-specific content rather than only on the model's training knowledge. This reduces invented statements, makes sources traceable and allows updates without retraining.

RAG
knowledge search
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