Fine-Tuning
Fine-tuning is the process of further training a pre-trained AI model on a narrower dataset to specialize its behavior, distinct from the retrieval that grounds live answers.
Fine-tuning specializes a general model on focused data. Starting from a pre-trained large language model, you continue training it on a smaller, targeted dataset so it adopts a particular style, domain, or task. The base knowledge stays; the behavior is nudged.
For AEO, fine-tuning is worth understanding mainly to separate it from retrieval. Being cited in an answer engine comes from grounding in retrieved sources, not from being in a model's fine-tuning set — so AEO targets retrieval, not training. Where your original work does influence models is through the broader training data pipeline over time, which is one more reason that genuinely original content, found nowhere else, is valuable.
Example. A company might fine-tune a model on its support transcripts to build a help bot. That's a product decision; it has little to do with whether a public answer engine cites the company's website, which is governed by retrieval.
Relevant pillar
Related terms
- Training DataTraining data is the body of text and other content an AI model learns from during training, shaping what it knows by default before any live retrieval is involved.
- Large Language Model (LLM)A large language model is an AI system trained on vast amounts of text to predict and generate language, and is the engine that writes the answers in AI search.
- Knowledge DistillationKnowledge distillation is a technique for training a smaller, faster AI model to mimic a larger one, transferring much of its capability into a cheaper-to-run model.