I tried using Muse AI to find an approval rule in a recorded training session for my team. It pointed me to a relevant discussion, but when I watched more of the recording, the speaker added an exception that changed how the rule applied. I was putting together a reference sheet for coworkers who missed the session.
What tasks should I avoid giving Muse AI when missing context could lead someone to follow the wrong procedure? I’m not sure whether this counts as a search limitation or something else. How do you decide which requests need a full manual review of the recording?
I’d expect Muse AI to help locate a discussion, not certify that a procedure has no exceptions. Based on your example, I’d treat this as a context-completeness problem rather than necessarily a made-up answer. It found something relevant, but that passage wasn’t enough to support the instruction you needed. “Where did they discuss approvals?” and “When can my coworkers proceed without approval?” need different levels of verification.
I’d avoid giving it sole responsibility for turning training into final checklists, deciding whether a particular case qualifies, resolving conflicting instructions, or answering “Does this ever require approval?” Those requests depend on exceptions and qualifications. You can still ask it to gather candidate passages, including later corrections and Q&A, but I wouldn’t treat “no other mentions found” as proof that there aren’t any. For your reference sheet, I’d keep each rule paired with its conditions, exceptions, and supporting timestamps until someone verifies it.
My review threshold would be whether an omitted condition could change someone’s next action. If it could, manually verify the instruction before sharing it. That doesn’t always mean watching the entire recording: a clearly bounded section plus its related questions may be enough. I’d review the whole session when the discussion jumps around, later comments revise earlier guidance, or you can’t establish where the relevant discussion ends. If the recording still leaves ambiguity, ask the process owner to confirm the wording rather than turning an uncertain explanation into a definite rule.
“What did the trainer say?” and “What’s our current approval process?” are different questions. A review of the whole recording, as proposed, would not detect a change in the policy after the fact. I would not give Muse AI the second task with only the training recordings as a resource because, despite capturing all exceptions, it would not account for changes in the policy.
An up-to-date policy still wouldn’t make a quiz generated from that recording ready to grade. @pete68 is right about checking which policy applies, but I’d put “write the answer key and score my team” on the avoid list too. Your missing exception could turn into a question where the coworker who actually remembers the training gets marked wrong.
That’s a particularly annoying use of this kind of output because the mistake gets dressed up as a teaching point. Imagine a multiple-choice question asking whether approval is required, with “always” as the expected answer. Someone picks “it depends,” remembers the exception correctly, and gets feedback telling them to relearn the procedure. You’ve now created extra confusion instead of checking understanding.
I’d be much happier asking Muse AI for draft discussion questions: “What would you need to know before deciding whether to request approval?” That gives people room to name the conditions rather than guess which simplified rule the quiz expects. The draft could still miss something, but you haven’t handed it the authority to declare somebody’s answer incorrect.
For a scored assessment, I’d work backward from an answer key supplied by whoever owns the training. Have the AI suggest scenarios around those approved answers, with any assumptions spelled out. Don’t ask it to infer both the rule and the test of that rule from the same recording. And if someone challenges a quiz answer with a timestamp, leave that question unscored while it gets checked. A training quiz should help catch misunderstandings, including mistakes in the quiz itself.
Was the trainer talking through the rule and explaining it out loud, or was it shown on the screen somehow? This would affect what I would want Muse AI to generate for me. I would first have to check if the material it used to generate an answer included the part shown on screen, before asking to turn a demonstration into instructions.
@pete68’s distinction about current policy matters here, but I’d keep a separate check for the actual walkthrough. Even with the correct policy in hand, you still need to know which account, settings, and starting state the trainer used. A spoken instruction like “then choose this option” doesn’t identify the option by itself.
For screen-based training, I’d work through it this way:
Pick a short demonstration where the trainer points at something without naming it. Ask for the steps and compare the draft with what’s visible. This checks a specific dependency. Getting that example right wouldn’t establish that every other demonstration was captured.
Mark any step that depends on an unreadable label, an unexplained selection, or a setting the trainer never discusses. Leave a blank or a question there instead of asking the AI to fill in what “probably” happened.
Have the training owner check those gaps before anyone uses the draft as a walkthrough. If there’s a practice environment, have someone follow the checked instructions there without watching the original demonstration. That gives you a way to catch missing steps without using live work as the test.
I would add “make a standalone click-by-click guide from this recording” to the list of things to avoid if you haven’t double-checked that all the visuals are there and clear. I would be happier with a draft that highlights the steps that need screenshots or further explanation.
The exception says that it applies to situations where the rule as written is correct but the coworker couldn’t follow it. That’s a different type of error than the training handout example.