Fine-Tuning: Definition

Fine-Tuning

Also: fine tuning, model fine-tuning, LLM fine-tuning, fine-tuning vs RAG

Fine-tuning is further training a pretrained model on a smaller, focused dataset so it specializes in a task, tone, or domain. The model keeps its general knowledge but adjusts its weights toward your examples, which is why a fine-tuned model can sound on-brand or follow a niche format that prompting alone struggles to enforce.

Updated June 23, 2026

Fine-tuning is one of three ways to change what a model produces, and it is easy to confuse them. Training sets the base knowledge in the weights. Fine-tuning nudges those weights toward your examples. Retrieval changes the evidence the model sees at answer time without touching the weights at all.

For most publishers, fine-tuning is the wrong tool for AI visibility. Even where a vendor offers a fine-tuning API, it only changes your own private deployment, not the public ChatGPT or Gemini your buyers actually use. Fine-tuning matters when you run your own open-weights model and need consistent behavior. To influence what the big engines say about you, the lever is the evidence they retrieve, not their weights.