Fine-tuning
Also: fine tuning
More training on a chosen set of examples. It changes a model to suit a task or style.
Fine-tuning changes how the model writes, not what it knows about this week’s facts. For facts, give it the documents instead.
The fuller definition
Fine-tuning is additional training that changes some or all model parameters for a task, behavior, or domain.
Go deeper
Supply instructions and examples each time. Nothing in the model changes.
Fetch current documents and put them in context. Facts can change daily.
Train the parameters on examples. Changes behaviour and style; poor at keeping facts current.
Most legal tasks need the first two. Fine-tuning is a project, with data, evaluation, and maintenance, and it is the wrong tool for facts that change.
A concrete example
A developer trains on reviewed examples to make extraction outputs follow a consistent classification scheme.
The distinction that matters
Fine-tuning differs from retrieval and saved instructions. It is not automatically the best way to add changing facts.
Related terms
Training
Adjusting a model with examples and a learning rule. The aim is to improve how it handles tasks.
Documents & retrievalRAG — Retrieval-augmented generation
Letting the model look things up in your documents before answering, and show where the answer came from. Like an open-book exam.
Training & generationLow-rank adaptation (LoRA)
A cheap way to fine-tune: instead of changing all the model's numbers, you train a small add-on that sits beside them.
Where it is taught
How models learn from examplesSource and further reading
Hugging Face · LLM courseThe definition and example are written for this library. The link gives background, official documentation or legal authority as labelled. Legal scope depends on the jurisdiction and current text; product features depend on release and settings.