Supervised Fine-Tuning (SFT) — AI Glossary

Reference details

Related terms

• Fine-Tuning
• RLHF (Reinforcement Learning from Human Feedback)
• Direct Preference Optimization (DPO)
• Base Model

Example: What the training pair looks like

Not documents, but demonstrations: an instruction paired with the response you want. “Summarise this ticket in two sentences, ending with the next action.” → the ideal two-sentence summary. Ten thousand of those teach form and register. Feeding raw documents instead teaches the model to continue documents, which is not what you asked for.

Commonly confused with

SFT is not pretraining and not alignment. Pretraining learns language from raw text; SFT teaches instruction-following from curated pairs; RLHF or DPO then tunes preferences between acceptable responses. SFT sets the baseline behaviour the preference stage refines.

When to use it

Reach for it when:

• You need consistent instruction-following in a specific format or domain
• You have curated examples of the task done correctly
• It is the prerequisite step before any preference-based alignment

Reach for something else when:

• Your examples are inconsistent — SFT will learn the inconsistency faithfully
• The goal is factual knowledge rather than behaviour
• Prompting has not been exhausted; it is reversible and SFT is not

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