Step 1: Prepare training data
Create input/output example pairs in the format you want the model to learn
Step 2: Upload and configure
Import data into AI Studio and set hyperparameters (learning rate, epochs, batch size)
Step 3: Train
Tuning job runs on Google's infrastructure with real time loss curve monitoring
Step 4: Deploy and use
Your tuned model gets a unique API endpoint ready for production use
When to tune vs. when to prompt
Before investing in fine tuning, try prompt engineering with system instructions and few shot examples first. Tuning is most valuable when you need consistent formatting across thousands of calls or when the task requires domain specific patterns that prompting alone cannot achieve reliably.