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:
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Enforce consistency: Return a specific response style or non-standard data format every time.
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Improve efficiency: Some tasks can achieve similar performance on a smaller, cheaper and lower latency model that has been fine tuned.
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Specialize: In areas where training data is sparse, fine tuning can improve accuracy and reduce hallucinations.
As part of the Production-Ready AI series, we’re releasing two new hands-on labs that cover two different fine-tuning options on Google Cloud: the fully managed experience on Vertex AI and the fully customizable path on Google Kubernetes Engine (GKE).
Option 1: The Managed Path with Vertex AI
For many developers, the goal is to improve model performance with as little infrastructure overhead as possible. You want the “easy button” for adaptation.
Vertex AI provides a fully managed service for fine-tuning models like Gemini. You don’t need to worry about provisioning GPUs, managing checkpoints, or writing complex training loops. You simply provide your data, configure your parameters, and let Vertex AI handle the rest.
