What is LLM fine-tuning?
Fine-tuning is the process of taking a pretrained language model and continuing its training on a smaller, domain-specific dataset to specialize the model for a task or style. It bakes knowledge into the model weights, in contrast to RAG which keeps the knowledge in an external database.
When to fine-tune
Fine-tune when you need consistent style or format that prompting cannot reliably produce, or when you have a task-specific dataset that improves performance over what the base model offers. Fine-tune sparingly: the operational overhead is significant and updates require a fresh training run.
When not to fine-tune
If the goal is to make the model answer using your knowledge base, do not fine-tune. Use RAG. Fine-tuned knowledge is impossible to audit, expensive to update, and prone to drift. RAG keeps facts in a database where you can edit them in real time.
Cost shape
Full fine-tuning of a modern open-weight model can require a GPU cluster for hours. LoRA and QLoRA reduce this to a single GPU for a few hours by training only a small adapter on top of frozen weights. For most customer-support use cases, RAG plus prompt engineering reaches similar quality at much lower cost.
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