The four heights
The same task, four distances: today's deadline, the next reviewer, the stuck moment, the pattern.
Execute — do the immediate task
+I have a long interview transcript to transcribe. Use voice typing to dictate the transcript into a…
Execute — do the immediate task
+I have a long interview transcript to transcribe. Use voice typing to dictate the transcript into a new document, preserve speaker labels for Anna and Omar, fix obvious punctuation errors, and stop after 28 minutes so I can review the first section before continuing. Ladder L1
Pasted it? When the reply comes back, push once: ask it to sharpen the weakest part. — Did this prompt help?
Improve — make it easier to accept
+Before I run the whole interview with voice typing, make the output easier to edit: insert speaker…
Improve — make it easier to accept
+Before I run the whole interview with voice typing, make the output easier to edit: insert speaker labels every three turns, remove filler words like ‘um’ and ‘you know’ where they don’t affect meaning, and add timestamps every five minutes so reviewers can cross-check with the audio. Ladder L2
Pasted it? When the reply comes back, push once: ask it to sharpen the weakest part. — Did this prompt help?
Decide — diagnose the stuck moment
+I tried voice typing on two speakers in a noisy cafe and the tool mislabels Anna as Omar halfway…
Decide — diagnose the stuck moment
+The audio’s messy and I’m afraid the transcript will misattribute speakers
I tried voice typing on two speakers in a noisy cafe and the tool mislabels Anna as Omar halfway through. I’m afraid if I trust the automated transcript I’ll send incorrect quotes to the client and lose credibility. What’s the most likely cause of the misattribution and the safest next step to fix or verify the transcript without re-listening to the whole recording? Ladder L5
Pasted it? When the reply comes back, push once: ask it to sharpen the weakest part. — Did this prompt help?
Become — change the pattern
+Across projects I spend hours cleaning transcripts because voice typing inserts filler words,…
Become — change the pattern
+Automated transcripts keep leaving in verbal clutter and mislabels
Across projects I spend hours cleaning transcripts because voice typing inserts filler words, mislabels speakers, and misses timestamps. That kills my editing time and delays delivery. Which single practice will cut that cleanup time by half — a quick audio pre-clean, a short speaker ID intro on each recording, or a two-pass workflow where I do a fast automated run then a focused manual pass — and how do I adopt it consistently? Ladder L6
Pasted it? When the reply comes back, push once: ask it to sharpen the weakest part. — Did this prompt help?
Next to this one
Other word processor work people do in Google Docs.
Every task here came from the work, not from a feature list — which is why the prompts name what you want done and never the button that does it. The tool changes; the work does not.
Copyright © LLOS.ai · 2026 — original pedagogy, voice, and design — all rights reserved.
The rest of the map
Same library, five ways in.