Glossary
Fine-tuning is extra training that adapts a model on chosen examples. It changes how the model behaves, which is not the same as feeding it current material.
Updated 21 Aug 2026
Fine-tuning is often proposed where the real requirement is current information. Training on documents changes how a model behaves; it does not reliably make it able to quote them, and it has to be redone as the material moves.
Ask for a worked example on your own material, and the evidence needed to reproduce it.
We use “Fine-tuning” only where a product mechanism or an evaluation method backs it up, and we say when the behaviour depends on how a deployment is configured.
Pick a real piece of work, agree what a good answer looks like, then go through the results together.