> ## Documentation Index
> Fetch the complete documentation index at: https://studio-docs.prem.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Fine-Tuning Overview
> Use fine-tuning to create a custom model for your use case.
This is a general overview of fine-tuning and what it is. If you want to learn how to fine-tune a model, please refer to the [Fine-Tuning Guide](/finetuning/get-started).
***Prem Studio*** offers fine-tuning capabilities so you can create a custom model without needing to be a machine learning expert.
Fine-tuning an AI model serves several important purposes:
* Adaptation to specific tasks: Fine-tuning allows a pre-trained model to specialize in particular domains or tasks that may differ from its original training data.
* Improved performance: It typically enhances accuracy and relevance for specific use cases compared to using a generic model.
* Efficiency: Fine-tuning requires significantly less data and computational resources than training a model from scratch.
* Customization: Organizations can tailor models to their unique requirements, terminology, or style.
* Overcoming limitations: It can help address biases or gaps in the original model's knowledge for particular applications.
* Rapid development: Fine-tuning accelerates the deployment of AI solutions by building on existing model capabilities.
# How it works
1. Create a [Snapshot](/datasets/create-snapshot) of your [dataset](/datasets/overview).
2. [Fine-tune](/finetuning/get-started) your model on your [dataset](/datasets/overview).
3. [Evaluate](/evaluations/get-started) your model.
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# Next Step: Create a Fine-Tuning Job
Click here to learn how to create a fine-tuning job.
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# Do More With Fine-Tuning
Fine-Tune a model with a snapshot of a dataset.
Prepare you fine-tune model with experiments.
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# After Fine-Tuning
Click here to learn how to evaluate a model.