Semantic highlights
Embedding models can rank data based on semantic meaning, evaluating each individual segment on a spectrum to show its relevance throughout the artifact.
When scanning large amount of text, I want to be able to rank data based on meaning, so I can understand the significance of selected concepts easily.
- The flow of language: When we are able to rank content on specific parameters, we also make the flow of the language visible. This makes it easier to scan large amounts of text for the information you are searching for.
More of the Witlist
In Arc, a playful pinch interaction lets you quickly distill any webpage into a brief summary, capturing the essence of the content in moments.
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.
Guide users to understand what makes a good prompt will help them learn how to craft prompts that result in better outputs.
Presenting multiple outputs helps users explore and identify their preferences and provides valuable insights into their choices, even enabling user feedback for model improvement.
AI actions often take time to complete. To improve user experience, use descriptions of what is happening combined with basic animations that represent different types of actions.
Voice interfaces should dynamically adapt to user interruptions, seamlessly incorporating them into the conversation ensuring a fluid and responsive dialogue.