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1

Navigate to Run Prompt

Click on the “Run Prompt” button in the top-right corner to begin creating a prompt.Run PromptRun PromptRun Prompt
2

Assign Prompt Name

Assign a name to your prompt. This name will appear as a new in your dataset.
3

Choose Model Type

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 to learn how to create custom models.
4

Configure Prompt with Roles

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.

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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.

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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.
5

Configure Model Parameters (optional)

Adjust model parameters such as temperature, max tokens, top_p, and other settings to fine-tune the model’s behavior according to your needs.
6

Configure Tools (optional)

Add tools or functions that the model can use during execution. This enables the model to perform specific actions or access external capabilities.
7

Configure Concurrency

Set the concurrency level to control how many prompt executions run in parallel. Higher concurrency speeds up processing but may consume more resources.
8

Run Prompt

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.