Article summary
Automatic speech-to-text and meeting summary tools are maturing fast, but they’re still not a fire-and-forget replacement for human note-taking. Over the last few months, I’ve been experimenting with a simple pipeline:
Meeting → Recording → AI Transcript → AI Notes & Summary
Below are some observations and recommendations that I’ve found helpful in getting a good output from these methods.
Observations
1. Quality in, quality out
A transcript is only as good as its source audio. Laptop mics, HVAC rumble, or a teammate in a car on Bluetooth can result in missing words or entire sentences. Capture a recording of the meeting.
2. “Oh, that’s a great idea” …/s
Tone, sarcasm, and the eye-rolls you spot on Zoom don’t make it into transcripts. Inside jokes can read as stone-cold directives and urgent blockers can look casual. Keep that in mind when you skim the transcript later.
3. Who actually said that?
Hybrid rooms add echoes and side conversations, and today’s models have to guess when two people talk at once. Some tools try to label speakers, but only if mics stay consistent. Expect to tidy up attribution by hand.
4. “Phantom” quotes & missing bits
Models sometimes invent filler phrases during long pauses (like “right, right”) or drop whole lines when someone coughs. If the text feels off compared with your memory, trust your gut and check the recording.
5. Privacy matters
Make sure everyone knows you’re capturing audio and feeding the transcript into an AI.
Recommendations
- Always record first, then transcribe. Live captions are great for accessibility, but post-meeting transcription gives you time to clean up audio, re-run sections, or even compare two transcription services.
- Use real microphones. Clear, high-quality audio can help minimize the number of errors in a transcription.
- Optimize for fully-remote calls when you can. The mute/un-mute and turn-taking baked into Zoom or Teams calls can help keep the audio clear and straightforward. If you’re hybrid, keep cross-conversation to a minimum.
- Use speaker labels. Tools like MacWhisper can auto-assign names, but it’s worth double-checking and manually fixing speakers if the meeting is high-stakes.
- Prompt the AI for what you need. After you’ve patched obvious transcript errors, feed the text back into an LLM with a structure like:
- Decisions made
- Action items (owner • due date)
- Parking-lot questions
- Sanity-check the output. Look for sentences that feel out of character, technical terms misspelled or swapped, and agenda items that vanished. Trust your gut if something feels off.
- Iterate! AI note-taking is rapidly improving. Today’s tools may look clunky in six months (or even next week!)
Bottom Line
Text transcriptions and meeting summaries are fun tools to play with. However, as with all AI tools, it’s important to use them to augment, and not replace, how you handle meetings. Though they do cut down on the time I spend summarizing meetings on my own, I’ve found that they have a lot of room to improve. They need quite a bit of hand-holding to produce an output that sits right with me.