Hi everyone! 👋 I'm diving into my Iris.ai trial next week, and I'm really excited to put it through its paces. I come from a background of testing a lot of martech tools, especially for personalization and analytics, so I know that what you measure during a trial is everything.
I was hoping we could pool our experiences. Beyond the obvious "does it find relevant papers," what specific, measurable things should I be tracking? I'm planning to set up a small, real-world research project to test it against.
Here's my starter list of key metrics I'm thinking about:
* **Precision vs. Recall:** How many of the returned documents are actually useful (precision), and is it finding *all* the key papers I know should be there (recall)?
* **Time-to-Insight:** Clocking the hours from starting a research question to having a synthesized set of core papers or concepts.
* **Workflow Integration:** How many steps does it take to go from Iris.ai to my reference manager (like Zotero) or note-taking app? Does it break my flow?
* **"Connector" Accuracy:** When it maps concepts and suggests related research paths, how often are those leads genuinely valuable?
I'd love to hear what you all looked at. Did you measure the reduction in manual search time? The quality of the auto-generated summaries? Any pitfalls in the setup I should avoid? Sharing our trial frameworks would be so helpful for the community!