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
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.
Letting people select text to ask follow-up questions provides immediate, context-specific information, enhancing AI interaction and exploration.
Starting with a blank canvas can be intimidating, but providing prompt starters can help individuals overcome this initial hurdle and jumpstart their creativity.
Using the source input as ground truth will help trust the system and makes it easy to interpret its process and what might have gone wrong.
AI collaboration agents can act as writing partners that assist people by enhancing their content through transparent, easily understandable suggestions, while respecting the original input.
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.