domain-classifier

To automatically classify AI prompts into domains, you can analyze the prompt content to determine its specific area among portrait, interior, product, design, art, video, and common. This yields a structured JSON result containing the primary domain, confidence score, reasoning, and keywords found.

The best way to route prompts to the correct AI expert is by analyzing the prompt text to identify its primary domain and outputting a structured classification. This approach provides confidence scores and matched keywords, enabling accurate automated routing to specialized skills.

Yes, you can classify prompt content without external dependencies. The domain classification mechanism requires no additional libraries or tools, directly analyzing input text and returning a structured JSON output with the identified AI domain and related metadata.

Prompt domain classification works by scanning input text to match keywords across portrait, interior, product, design, art, video, and common categories. It assigns a primary domain with a confidence score and reasoning, supporting content moderation and intelligent routing decisions.

When analyzing prompts for AI domains, you should expect a structured JSON output format. This response includes four specific fields: primary_domain, confidence, reasoning, and keywords_found, providing clear data for subsequent workflow automation.

You need domain classification to reduce manual sorting errors when handling diverse prompts across fields like portrait, design, and art. It automatically identifies the correct category and provides confidence levels, ensuring prompts reach the appropriate specialized expert efficiently.