Evaluate predictions
When an observation is added to the context from an implicit action and a prediction is made, users should be able to easily evaluate and dismiss it.
When an observation is added to the context of the AI system or a conclusion is reached, I want to evaluate and dismiss it easily, so I can ensure the information is accurate and relevant to my needs.
- Transparency in Knowledge Gathering: Make it easy for users to understand the factors that influence and shape the knowledge being gathered. When new information is added to the context, clearly communicate this to the user.
- Control over Assumptions: Provide a simple way for users to dismiss or challenge assumptions to ensure the accuracy and reliability of the knowledge base.
More of the Witlist
Presenting multiple outputs helps users explore and identify their preferences and provides valuable insights into their choices, even enabling user feedback for model improvement.
Proactive agents can autonomously initiate conversations and actions based on previous interactions and context providing timely and relevant assistance.
LLM’s are great at organizing narratives and findings. It's helpful to see the sources that support these conclusions, making it easier to understand the analysis and where it comes from.
You should control how products and services (not) access your data through a manageable profile. This allows you to create a relevant context across many platforms while maintaining control.
Empower users to make decisions and give feedback quickly or engage more deeply when needed in natural language.
Ordering content along different interpretable dimensions, like style or similarity, makes it navigable on x and y axes facilitating exploration and discovery of relationships between the data.