GLOSSARY
Fine-tuning
Fine-tuning adapts an already trained model through further training on selected examples, changing model parameters for purposes such as tasks, style or output formats.
Adapt a recurring task deliberately
A sales team may need to turn similar tender questions into a consistent response format. Fine-tuning can be an option when enough suitable examples are available and their use is permitted. The work starts before training: good and poor responses must be distinguishable, exceptions included and the intended output specified. Unchecked historical proposals may introduce errors or outdated commitments into the adaptation. A large collection of examples therefore cannot replace defensible quality criteria.
Compare simpler approaches first
Before deciding, compare improved instructions, templates and retrieval of current information. Fine-tuning is not automatically a suitable way to maintain frequently changing prices or contract versions. Those facts still need a reliable source. An evaluation uses cases excluded from the adaptation and checks for deterioration elsewhere as well as the intended improvement. Economics should include data preparation, training, operation and future maintenance. A lasting adaptation becomes a credible investment only when the additional benefit justifies that effort within the actual workflow and the resulting responsibilities are manageable.