# Prompt Dataset Methodology

Methodology for the GPT Image Hub public prompt dataset, including fields, provenance, update cadence, and machine-readable distributions.

URL: https://www.gptimagehub.com/prompt-dataset-methodology
Updated: 2026-10-21

## Dataset scope

The public dataset contains prompt templates that are visible in the GPT Image Hub library and intended for discovery, retrieval, and reuse.

- Each record includes full prompt text and canonical URL.
- Each record includes category, tags, model defaults, aspect ratio, and attribution fields.
- Translations are included when available so agents can map localized prompt intent.

## Formats and discovery

The dataset is available in multiple machine-readable formats for search engines, AI agents, and data pipelines.

- JSONL is the recommended format for bulk ingestion.
- CSV is available for spreadsheets and BI tools.
- A JSON Schema and manifest describe field semantics and distributions.

## Provenance and freshness

Records expose source fields when known and include timestamps so agents can evaluate freshness and provenance.

- The manifest includes version, updated\_at, provenance, and same\_as fields.
- Manifest, schema, and dataset page responses include content-derived ETags; each record carries its own updated\_at timestamp.
- Category-level distributions allow smaller targeted crawls.

## Public resources

GitHub and Hugging Face contain a licensed subset of the live library. Follow each record’s attribution and license; images have separate rights.

- [GitHub dataset](https://github.com/jqy-yo/gptimagehub-prompts): Attributed prompts, a preview gallery, and generated examples.
- [Hugging Face dataset](https://huggingface.co/datasets/jqyyyy/gptimagehub-prompts): Browse and download the same licensed prompt snapshot.
- [MCP Registry](https://registry.modelcontextprotocol.io/?q=io.github.jqy-yo%2Fgptimagehub): Find our registered MCP service and connect your AI tools.