Write One-on-One Talking Points with AI
Five minutes before a 1:1, paste what's on your mind into @vustbot — wins, friction, asks, last meeting's leftovers — and request prioritized talking points with a suggested opener for the hard item. The model organizes your own raw material into a conversation plan; nothing about the other person is fetched from anywhere.
The worst 1:1s aren't hostile — they're empty, both sides improvising pleasantries for thirty minutes. The fix is embarrassingly cheap: five minutes of dumping your actual topics into a model and letting it sequence them, hard thing included.
What the AI does in this scenario
- Raw brain-dump in, prioritized conversation plan out
- The difficult topic gets a suggested opener, not a euphemism
- Carry-over slot for what you promised last time
- Works both directions: manager preparing, or report managing up
Worked example: write one-on-one talking points with ai
01Input — what you paste
For 1:1 with my manager: shipped the migration early. annoyed that my design doc sat unreviewed 2 weeks. want to ask about leading the payments workstream. also she mentioned a reorg rumor last time, follow up.
02Output — what comes back
Talking points (30 min): 1) Open with the win — migration shipped early, one sentence, don't over-sell. 2) Follow-up owed: the reorg rumor she raised. 3) The ask: payments workstream lead — tie it to the migration result. 4) The friction (opener: "Can we talk about review turnaround? My design doc waited two weeks — what would make that faster?"). Close: agree one concrete next step on #3.
How to write one-on-one talking points with ai — step by step
- 1Brain-dump without editing
Everything on your mind about this person and this working relationship, unsorted, into @vustbot. Ordering while dumping is exactly the cognitive load you're delegating.
- 2Ask for priority plus one opener
Have the model sequence the items for a real conversation arc — win, business, the ask, the hard thing — and draft an opening sentence for whichever item you're most likely to chicken out of.
- 3Bring three points, not ten
Cut the generated list to what fits the slot; a 1:1 is a conversation, not a review meeting. The discarded items seed next time's dump.
AI vs doing it manually
Preparing 1:1s manually works when you already keep a running topics doc — many great managers do, and a model adds little to that discipline. The realistic case is the other 80%: walking to the meeting assembling thoughts in the corridor. There, the AI's contribution isn't insight — it's sequencing and nerve. It puts your buried "I want the payments lead" third instead of never, and hands you a non-confrontational opener for the friction item. The content was always yours; what the model removes is the improvisation tax.
The prompt to copy
Turn these raw notes into talking points for a [LENGTH]-minute one-on-one with my [MANAGER/REPORT]. Prioritize into max 4 items with a suggested order: quick win first, hardest item second-to-last. For the most difficult item, draft a neutral opening sentence. Add a "carry-over" slot if my notes mention past promises. My notes: [PASTE RAW THOUGHTS]
Frequently asked questions
Should I paste sensitive feedback about a colleague into the bot?
Keep it functional: "friction on review turnaround" prepares the conversation just as well as a named rant, and your talking points shouldn't read like a dossier anyway. Paste situations and behaviors, not personnel-file judgments — the model sequences topics; the sensitive framing stays in your head.
Can AI prepare talking points for a 1:1 where I have to give hard feedback?
It can structure it — situation, behavior, impact, then a question — and draft the opener, which is genuinely the hardest sentence. What it can't do is calibrate to the relationship: how direct this person can hear. Take the structure and the opener; deliver the middle in your own words.
What if my 1:1 notes are just three vague feelings?
Paste the three vague feelings — "annoyed about reviews, worried about the reorg, want more scope" is a complete input. Ask the model to turn each into a concrete discussable item with a question attached. Vague-to-specific is a transformation models do well; blank-page-to-anything is the part that was blocking you.
Related in Meetings
Try it on your real task
The welcome bonus covers a first run — send the prompt above with your own facts and judge the output yourself.
Open @vustbot