Incentivize giving feedback
Presenting multiple outputs helps users explore and identify their preferences and provides valuable insights into their choices, even enabling user feedback for model improvement.
When generating outputs, I want multiple suggestions to explore and identify my preferences, so I can choose the best option.
- Personalized Outputs through Progressive Refinement: The process of making suggestions and progressively narrowing down options can lead to more personalized and satisfactory outputs.
- Ideal for Uncertain User Preferences: This approach is ideal for situations where users may not fully know what they want. Many tools that take a longer time to generate output provide at least two options to choose from. For example, Suno and Midjourney do this.
- User Preference Feedback: Allowing users to pick between multiple outputs informs the model which one is preferred.
More of the Witlist
Embedding models can rank content along virtually any dimension. This capability provides significant value by enabling users to explore and analyze the embeddings to create a spectrum of any features.
In Arc, a playful pinch interaction lets you quickly distill any webpage into a brief summary, capturing the essence of the content in moments.
Voice interfaces should dynamically adapt to user interruptions, seamlessly incorporating them into the conversation ensuring a fluid and responsive dialogue.
Referencing nested data from your database in the form of tags can simplify the creation of elaborate prompt formulas.
AI excels at classifying vast amounts of content, presenting an opportunity for new, more fluid filter interfaces tailored to the content.
Embedding models can rank data based on semantic meaning, evaluating each individual segment on a spectrum to show its relevance throughout the artifact.