Mastering Model Adaptation: A Guide to Fine-Tuning on Google Cloud

If you are building AI applications , you might experiment with prompts, or even dip your toes into agents . But as you move from prototype to production, you might hit a common wall: the model is just not as consistent as you need it to be. Gemini is an incredibly capable universal foundation model, but you might want responses to adhere to brand style guides more consistently, or maybe you need to ensure that an API is formatted in a custom, non-standard JSON format every single time.  In many cases, prompt engineering and in-context learning will be enough to get the results you want. However, as you move toward more specialized production requirements, you might want to push your model even further. This is where fine-tuning comes in. Fine-tuning allows you to take a general-purpose model like Gemini 2.5 Flash or an open-source model like Llama and adapt it to your specific domain. By training the model on a curated dataset of your own examples, you can: Enforce consistency : Retur