As a Recall user,

I want advanced AI-driven recommendations for tags and links when creating or updating knowledge cards,

So that I can enrich my knowledge base with more accurate connections and categorizations, improving the discoverability and utility of the information I collect as it continues to grow in size, reducing the amount of manual updates and links needed.

Acceptance Criteria

Inferred Relationships Discovery: The system automatically suggests potential relationships between knowledge cards based on the content, existing links, and tags within the knowledge base, when a card is created or upon user request for existing cards.

Dynamic Tag Recommendations: When a new knowledge card is created or during review of an existing card, the system suggests relevant tags based on the card's content and the context of existing tags within the knowledge base, enhancing content categorization.

Link Recommendations: Upon creation of a new card or through user initiation on an existing card, the system suggests potential links to other knowledge cards that share related content or concepts, facilitating deeper connections within the knowledge base.

Actionable Suggestions: Users receive actionable suggestions that they can accept, modify, or reject, allowing for seamless integration of recommended tags and links into their knowledge cards.

Continuous Learning: The recommendation system learns from user actions, improving the relevance of its suggestions over time based on user feedback and interaction patterns.

User Control and Customization: Users have control over the recommendation settings, including the ability to prompt the system for suggestions at any time and customize the criteria used for generating recommendations.