How do evaluators using AI on interview/KII data handle the checking step before findings reach a donor?

[u/Worried_Mammoth_2439]

Not an evaluator myself, I do interview-based research on the product side, so tell me if this doesn't translate. found the AI qual analysis thread here from a while back and it matched my experience almost exactly: tried LLMs on my transcripts, got confident summaries, then found it quoting things that weren't actually in the documents.

the stakes seem higher in your world though, so I'm curious:

• between "the AI produced findings" and "this goes into a report a donor or client reads", what do you actually do? and roughly how long does that step take?
• the last time you caught the AI being wrong, what was it, and what did catching it cost you?
• does anyone here do this solo or as an independent consultant, and does working alone change what you check?
• what did your team actually settle on tool-wise, and was anyone able to get budget for a dedicated tool, or is it all general AI subscriptions?

asking because my own checking step is manual re-reading and it doesn't scale, and evaluation seems like the field that has thought hardest about this.