Data Transformation: Categorizing System Prompts

Derive categorical data from unstructured text fields to better understand dataset composition.

Overview

Starting with a large conversation dataset (Orca), analyze system prompts and create a categorical column to classify prompt types.

Steps

1. Load the conversation dataset

   Open OpenOrca/partial-train/0000

2. Examine the data

   Review the system_prompt column containing system prompts

   > Note: See the different kind of system prompts

3. Create categorical transformation

   In chat, request: "create a new column that categorizes the system prompt"

   > Note: Hyperparam analyzes system prompts and generates categories (e.g., "Education/Tutor", "General Assistant", "Informaton retrieval/QA")

   New column system_prompt_category appears with classifications

4. Review categorization

   Scroll through rows to verify category assignments

   > Note: Categories help identify prompt patterns across the dataset

5. Export categorized dataset

   Click export

   Select columns including system_prompt_category

   Export processes full 100k+ dataset

Expected Results

• New categorical column: system_prompt_category classifying each system prompt
• Pattern insights: Understand distribution of prompt types in the dataset
• Enhanced metadata: Enables filtering and analysis by prompt category

Other Use Cases

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• Quality Filtering - Remove low-quality responses from datasets
• Deep Research — Multi-step AI workflow for dataset research and model comparison