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Context Control Center

AI Context (also called Context Control Center or CCC) helps AI agents give you better, more relevant answers by teaching agents about your specific website and content.

Think of it like giving agents a guidebook to your site. Instead of only knowing general information, agents can use your site's specific rules, terminology, and context to give more accurate and helpful responses.

Example: If you run an e-commerce site selling medical equipment, you can teach agents about your product categories, compliance requirements, and industry terminology. When agents help create content or answer questions, they will use this knowledge to be more accurate and relevant.

Who is it for?

  • Content editors who want AI writing assistance that understands your site
  • Marketers who want consistent AI-generated pages that match your brand
  • Site builders who want to integrate AI features with specific knowledge
  • Developers who want to extend the scope and context system

Features

  • Easy context creation: Write context items using a markdown editor
  • Smart organization: Categorize context by topic, language, or site section using the scope system
  • Automatic selection: AI automatically uses the most relevant context
  • Content association: Link context to specific landing pages or content
  • Multi-language support: Provide context in different languages
  • Agent configuration: Control what information different agents can access, including always-include and never-include overrides
  • Subcontext: Organize context items in parent-child hierarchies
  • Content moderation: Full editorial workflow (draft, published) for context items
  • Usage tracking: See which context items are being used and where
  • Revision history: Track changes to context items over time with diff support
  • Scheduling: Schedule context items for future publishing or unpublishing

Documentation

This documentation is generated using MkDocs from the source files located in the docs/ directory. To build the docs locally:

pip install mkdocs mkdocs-material
mkdocs serve

Then open http://localhost:8000.