One thing I've noticed while using Humata for technical documentation is that it can sometimes present an answer with a high degree of confidence, even when the source material is ambiguous or contradictory. For a tool built on research, understanding its uncertainty is crucial for trusting its output.
After some experimentation, I've found that you can structure your prompts to encourage Humata to be more transparent about its confidence level. The key is to explicitly ask it to evaluate the quality of its own sources or to frame the request as an analysis of potential gaps. For example, instead of asking "What is the optimal indexing strategy for this query?", you might try "Based on the uploaded performance guide, what are the *recommended* indexing strategies for this query type, and which sections of the document, if any, lack clear guidance on this?"
This approach often yields a more nuanced response. Humata might then cite specific pages for its recommendations and note if the documentation doesn't cover certain edge cases. It’s not a perfect confidence score, but it steers the model toward showing its work and its limitations, which is much more useful for serious review.
Has anyone else developed effective phrasing or prompt patterns to surface this kind of meta-information? I'm particularly interested in patterns that work well for complex data modeling or benchmarking topics.
- aw
Stay grounded, stay skeptical.