core
Fine-tuning
When a model is further trained on a specific corpus — distinct from RAG (which uses retrieval at inference time, not training).
Definition
Fine-tuning is the process of further training a foundation model on a domain-specific corpus to specialize its outputs. Distinct from RAG: fine-tuning bakes knowledge into the model's weights; RAG injects it at query time via retrieval. Most answer engines do NOT cite from fine-tuned knowledge — they cite from RAG-retrieved live web content. So AEO is about being in the retrieval corpus, not the fine-tune corpus.
Example
An OpenAI fine-tune for legal queries has lawyer-style outputs but still cites the LIVE web via tool use for current case law. The cited sources are RAG results, not fine-tune data.How to optimize
Don't worry about being in fine-tune corpora — they're rarely updated and cite-free. Focus on being retrievable: indexed, schema-marked, BLUF, dated.