> For the complete documentation index, see [llms.txt](https://aizen-corp.gitbook.io/docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://aizen-corp.gitbook.io/docs/managing-llm-workflows/creating-training-datasets-for-llms.md).

# Creating Training Datasets for LLMs

A training dataset is required for you to fine-tune an LLM. To create a training dataset, follow these steps:

1. Log in to the Aizen Jupyter console. See [Using the Aizen Jupyter Console](/docs/getting-started/using-the-aizen-jupyter-console.md).
2. Create an ML project if you have not already done so or set the current working project.

   ```
   create project <project name>
   ```

   or

   ```
   set project <project name>
   ```
3. Configure the dataset by running the `configure dataset` command:

   ```
   configure dataset
   ```
4. In the notebook, you will be guided through a template form with boxes and drop-down lists that you can complete to create features for the dataset.
   * If the input to the LLM is a single column in the dataset, then that column can contain the entire input text, including the prompt, or you can configure a prompt template separately during fine-tuning.
   * If the input to the LLM is two or more columns from the dataset, then you must configure a prompt template separately during fine-tuning.
5. Create the training dataset using the `start dataset` command to schedule a job. Optionally, you can configure resources for the job by running the `configure resource` command. If you do not configure resources, default resource settings will be applied.

   ```
   configure resource
   start dataset <dataset name>
   ```
6. Wait for the job to complete, and then check your training dataset:&#x20;

   ```
   status dataset <dataset name>
   list datasets
   display dataset <dataset name>
   ```