Definition
Fine-tuning starts with a pre-trained model and updates it with additional examples. The goal is to make the model follow a domain pattern more consistently than it would from prompting alone.
How it works
Teams gather high-quality examples, train the model on those examples, evaluate outputs against a benchmark, and monitor for drift or overfitting after deployment.
Why it matters at work
Fine-tuning can improve consistency, but it is not always the first answer. Many workplace needs are better solved with prompt templates, retrieval-augmented generation, or better source data.
Workplace example
A support organization fine-tunes a classifier to route tickets by product area, while using RAG for policy answers that change frequently.
Frequently Asked Questions
When should a company fine-tune a model?
Fine-tune when you have stable, high-quality examples and need consistent behavior at scale. Avoid fine-tuning for knowledge that changes often; retrieval is usually safer for that.