Using Run Prompt
The Run Prompt feature allows you to create dynamic column type by using custom prompts for LLM. Following steps are required to configure and execute prompts effectively
1. Select a Dataset
Choose a dataset from the available list to use for prompt creation. If no dataset appears on the dashboard, ensure you have completed the required steps to Add Dataset on the Future AGI platform.
2. Access the Run Prompt Interface
Once your dataset is loaded, you can view it in a spreadsheet-like interface. Click on the Run Prompt button in the top-right corner to begin creating a prompt.
3. Configure Your Prompt
Basic Configuration
To set up a prompt, configure the following details:
- Prompt Name: Enter a clear, descriptive name that reflects the purpose of the prompt.
- Model Selection: Choose the appropriate LLM model from the dropdown menu.
API Key Setup
To interact with the selected model, an API key is required. Follow these steps:
- Once a model is selected, a popup window will appear prompting you to enter your API key.
- Enter the key to enable communication between your dataset and the model.
- In this example, we are using GPT-4o, but other models may be available depending on your platform.
Output Configuration
The output format determines how responses are structured. Choose from the following options:
- String: Generates simple text responses (e.g., “correct” / “incorrect”).
- Object: Produces structured JSON outputs, useful for complex responses.
Make sure to select the format that best suits your use case.
Writing Your Prompt
You can dynamically access dataset columns within your prompt using double curly braces.
How it Works
- When writing your prompt, type
{{to trigger a dropdown menu displaying all available columns. - Select a column name from the list; it will be automatically enclosed in double braces (e.g.,
{{column_name}} - The model will replace these placeholders with the actual data from the dataset when generating responses.
This allows you to create dynamic prompts that reference dataset values without manually inputting them for each row.
4. Adjust Model Parameters
Tuning model parameters is crucial for optimising performance. Below are the key parameters and their effects:
| Parameter | Description | Impact |
|---|---|---|
| Concurrency | Number of simultaneous prompt executions | Higher values increase speed but may hit API limits |
| Temperature | Controls randomness of responses | 0: Deterministic, 1: More creative but less predictable |
| Top P | Controls diversity in token selection | Lower values keep responses focused, higher values introduce variation |
| Max Tokens | Defines maximum response length | Higher values allow longer responses but increase API usage |
| Presence Penalty | Adjusts topic diversity | Higher values encourage diverse topics, lower values keep responses on a single topic |
| Frequency Penalty | Reduces word/phrase repetition | Higher values discourage repetition, lower values allow it |
Response Format
- Choose between text or JSON output format.
- Configure tool interaction settings:
- Required – Forces the model to use tools
- Auto – Allows the model to decide
- None – Disables tool interaction
5. Execute the Prompt
- Click Save and Run to execute your prompt configuration.
- The generated responses will be stored in a new column named after your prompt.
Best Practices for Prompt Execution
To ensure the best results, follow these guidelines:
- Start with low concurrency to prevent hitting API rate limits.
- Use temperature 0.0 - 0.3 for factual, structured responses.
- Use temperature 0.7 - 1.0 for creative and open-ended tasks.
- Set reasonable max token limits to optimise cost efficiency.
- Run prompts on a small subset of data before applying them to the full dataset.
By following these best practices, you can effectively create dynamic columns using Run Prompt while maintaining efficiency and accuracy in your AI-powered workflows.