Hi everyone,
I had a question while exploring how Rovo retrieves information.
Let's say I ask a question that matches a lot of content across Jira issues and Confluence pages. Since every LLM has a context limit, I'm wondering what happens when there's simply too much information to include in a single response.
Does Rovo summarize the retrieved content first, select only the most relevant documents, or use some other approach before sending it to the model?
I'm mainly trying to understand how Rovo keeps responses relevant without overwhelming the model when there's a large knowledge base behind it.
Would love to hear if anyone has insights into how this works.
Thanks!