> ## Documentation Index > Fetch the complete documentation index at: https://futureagi.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Run Prompt in Dataset > Learn how to execute prompts against your dataset and generate responses Click on the "Run Prompt" button in the top-right corner to begin creating a prompt.

Assign a name to your prompt. This name will appear as a new dynamic column in your dataset. Select the model type based on your use-case. Choose "LLM" to generate text responses using general-purpose LLM models. Recommended for everyday use-cases. LLM

Click [here](/future-agi/get-started/evaluation/use-custom-models) to learn how to create custom models. Text-to-Speech

Click [here](/future-agi/get-started/evaluation/use-custom-models) to learn how to create custom models. Speech-to-Text

Click [here](/future-agi/get-started/evaluation/use-custom-models) to learn how to create custom models. Image Generation

Define your prompt using roles. You can configure messages with different roles: * **User Role (Required)**: The main input message from the user perspective. This role is required for the prompt to work. * **System Role (Optional)**: System-level instructions that guide the model's behavior and set the context. ### Using Variables You can reference dataset columns as variables within your prompt using the `{{ }}` syntax. Simply wrap the column name in double curly braces: **Basic Example:** ``` System: You are a helpful assistant that summarizes content. User: Please summarize the following text: {{column_name}} ``` The variables (column names) will be dynamically replaced with actual values from your dataset when the prompt runs. ### JSON Dot Notation For JSON type columns, you can access nested fields directly using dot notation. This allows you to reference specific keys within structured data without additional processing: **JSON Example:** ``` User: Based on this prompt: {{column_name.key_name}}, generate a response that addresses {{column_name.key_name}} ``` In this example: * `{{column_name.key_name}}` accesses the `key_name` field within the `column_name` JSON column This feature significantly simplifies complex data handling and speeds up setup when working with structured JSON data in your dataset. Adjust model parameters such as temperature, max tokens, top\_p, and other settings to fine-tune the model's behavior according to your needs. Add tools or functions that the model can use during execution. This enables the model to perform specific actions or access external capabilities. Set the concurrency level to control how many prompt executions run in parallel. Higher concurrency speeds up processing but may consume more resources. Click the "Run" button to execute the prompt across your dataset. The responses will be generated and saved as a new dynamic column in your dataset.