AI can only take your work so far if you do not know how to guide it. Asking it to make an answer better or try again may help, but useful feedback eventually needs to explain what is missing and why it matters.
Putting a large set of project documents into a tool can produce an output. Knowing whether that output helps with the work in front of you requires a different kind of understanding.
Explain what needs to change
For an estimate, that might mean spotting a gap in scope or recognizing that a benchmark does not fit the project. Without that understanding, it is difficult to explain what needs to change or judge whether the next answer is any better.
Specific feedback gives the next attempt a clearer purpose. The team can identify the missing scope, point to a relevant source, or explain why a comparison needs reconsideration. It can then assess whether the revised answer addresses that issue.
Keep learning and checking the work
AI can help people learn and make unfamiliar work more approachable. Nobody needs to know everything before using it, but teams do need to recognize when to check a source, ask someone with more experience, or take another approach.
Even a clear request can produce an incorrect answer. Improving the instructions does not remove the need to review the result or decide whether it is suitable for use.
Getting better at using AI also means continuing to learn the work it is being asked to support. That knowledge helps people ask more useful questions and recognize when an answer still needs attention.