What it means
Additional training (full or lightweight) on your labeled data so the model’s default behavior skews toward your product’s patterns.
Why designers should care
When prompts and few-shot examples plateau, fine-tuning may be needed, but you must plan versioning, evals, and rollback because behavior shifts in subtle ways.
Example
A healthcare portal fine-tunes on approved patient-facing phrases; the UI still shows “Draft: clinician review required” because tuning reduced but did not eliminate risk.
Common mistakes
- Expecting fine-tuning to replace guardrails or human review in regulated domains.
- Shipping a tuned model without A/B UX for regression on edge cases.
- Confusing fine-tuning with RAG. Retrieval adds facts; tuning changes style and priors.
Frequently asked questions
What is Fine-Tuning?
Fine-tuning adapts a base model to your domain, tone, or task by training on curated examples, beyond what a system prompt alone can reliably enforce.
Why should designers care about Fine-Tuning?
When prompts and few-shot examples plateau, fine-tuning may be needed, but you must plan versioning, evals, and rollback because behavior shifts in subtle ways.
What are common mistakes with Fine-Tuning?
Expecting fine-tuning to replace guardrails or human review in regulated domains; Shipping a tuned model without A/B UX for regression on edge cases; Confusing fine-tuning with RAG. Retrieval adds facts; tuning changes style and priors.