Semantic ranking
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.
When searching for information, I want to rank content semantically so I can quickly access the most relevant information and make insightful comparisons.
- Enhanced Information Retrieval: By ranking content based on semantic relevance, users can quickly access the most pertinent information, fostering creative ways of searching.
- Insightful Comparisons: Displaying results along a ranked spectrum facilitates comparison of relevant attributes, providing valuable insights into the relationships and similarities between different pieces of content.
- Relevancy Thresholds: A semantic ranker can incorporate a relevancy threshold to exclude results that are highly irrelevant, ensuring higher quality and more useful outputs.
More of the Witlist
Generative AI can provide custom types of input beyond just text, like generated UI elements, to enhance user interaction.
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.
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.
Referencing nested data from your database in the form of tags can simplify the creation of elaborate prompt formulas.
Embedding models can rank data based on semantic meaning, evaluating each individual segment on a spectrum to show its relevance throughout the artifact.
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.