A concrete example
You feed it thousands of already-validated classification examples so the model reproduces that labeling more reliably.
Why it matters
It can sharpen a style or a repeatable task, but it costs time and needs good data.
You see it on AI platforms, open-weight models, and projects with lots of high-quality examples.
Don't mix it up with
RAG: RAG lets an AI answer using documents pulled up at the moment you ask the question.
Evaluation / Eval: An eval regularly measures whether an AI answers correctly on representative cases.
Common mistakes
- Fine-tuning to add knowledge that changes often.
- Using a handful of weak examples.
- Forgetting to measure before/after with an eval.
Quick checklist
- First I check whether the word names a concept, a tool, a risk, or a metric.
- I tie it to a concrete case: You feed it thousands of already-validated classification examples so the model reproduces that labeling more reliably.
- I keep the main trap in mind: Fine-tuning to add knowledge that changes often.
Quick questions
What is Fine-tuning in AI?
Fine-tuning adapts an existing model on examples to improve one specific behavior.
Where will I run into Fine-tuning?
You see it on AI platforms, open-weight models, and projects with lots of high-quality examples.
Which word should I read next?
Start with RAG, Evaluation / Eval, LoRA / QLoRA.