Fine-Tuning vs Prompting for AI Agents
Two ways to change an agent's behaviour — and a simple rule for which to reach for first.
Fine-tuning and prompting are the two main ways to change how an AI agent behaves. Prompting (and prompt optimization) adjusts the instructions at inference time — fast, cheap and reversible. Fine-tuning trains the model on your data — more powerful for hard-to-prompt consistency, but slow, costlier and harder to roll back. The rule of thumb: prompt first, and fine-tune only when prompting has clearly hit its ceiling.
What's the difference between fine-tuning and prompting?
Prompting changes what you tell the model at runtime — instructions, examples, structure — without touching the model's weights. Fine-tuning changes the model itself, training it on examples so the behaviour is baked in. Prompting is iterated in minutes and reverted instantly; fine-tuning takes a training run, a dataset and evaluation, and rolling it back means redeploying a previous model. They operate at different layers and costs.
When should you fine-tune an agent instead of prompting?
Fine-tune when prompting genuinely runs out of road: when you need consistent formatting or behaviour the prompt cannot reliably enforce, when prompts have grown so long they dominate cost on every call, or when you need the model to internalise domain style or knowledge that examples alone do not capture. If a prompt change can fix the failing eval, do that — it is cheaper and reversible. Let the evals show prompting has plateaued before you commit to training.
Can you use both — and how do you decide?
Yes; they are complementary. A common pattern is to fine-tune for stable base behaviour and prompt for task-specific steering on top. Decide the same way you make any optimization choice: against evals. Try the cheaper lever (prompting) first, measure the gain on your golden dataset, and only escalate to fine-tuning when the data shows the cheaper lever cannot close the gap — then re-evaluate to confirm the trained model actually beat the prompted one in production.
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