What Is Fine-Tuning?
Fine-tuning continues training a pre-trained model on a smaller, focused dataset so it adapts to a specific task, domain, or style. It changes the model’s weights, unlike retrieval, which leaves them fixed and supplies knowledge at query time. Fine-tuning is the right tool for teaching a model how to behave — a format, a tone, a skill — and the wrong tool for teaching it facts that change, which belong in retrieval.
Behaviour versus knowledge
The common mistake is to fine-tune a model to make it "know" a company’s current data. Facts move; weights are expensive to update; and a fine-tuned fact cannot cite its source. Retrieval handles changing knowledge far better, by fetching it fresh at query time.
Where fine-tuning earns its cost is behaviour: reliably producing a structured output, adopting a house voice, or performing a narrow task with less prompting. For an agent, that often means a smaller, cheaper model fine-tuned for one job inside a larger system.
Related standards
Questions
Does fine-tuning make a model smarter?
No. It specialises an existing model; it does not add general capability beyond what the base model has.
Is fine-tuning better than RAG?
Neither is better in general — they solve different problems, and many systems use both. See the comparison.
Keep reading
By Michael Gord · published 2026-10-09 · part of the Agentic Encyclopedia. Dates are the day of publication; events are cited at their own dates.