ChatGPT drafts run sheets, vendor emails, timelines and venue comparison tables in minutes. It can also invent the facts an event rests on: capacities, licence rules, prices, opening hours and availability. Treat every number, rule and name it supplies as unchecked until the venue, supplier or licensing body has confirmed it.
The danger isn't random nonsense. It's plausible detail. A model asked to compare three venues for 150 guests will return a tidy table with a capacity, a hire fee and a finish time in every cell, including cells it had no way of knowing. OpenAI's help page warns that the model may express high confidence even in incorrect answers, and its terms of use tell people not to rely on output as a sole source of truth. A planner who pastes that table into a client proposal owns every invented number in it.
What it does well, in the order a planner meets it
Planning has a long paper trail, and most of it is text that ChatGPT handles well once you supply the facts. Roughly in the order the work arrives:
- The brief. A rambling client call becomes a one-page brief: purpose, date options, headcount range, budget, must-haves, unknowns.
- Questions for the client, the venue and suppliers. Say the client's description is "relaxed, maybe a barbecue, about 60 people in June, budget flexible". The list of what that call left unanswered typically covers a wet-weather plan, dietary and access needs, whether alcohol will be served and who holds the licence, how late music can run, how guests arrive and leave, who clears up, and who makes the final decision. Every item is a question, not a fact. That is the pattern worth keeping.
- Budget skeletons. Category headings and the usual line items, without amounts. A spreadsheet does the maths.
- Run sheets and timelines, built from fixed times you supply.
- Vendor emails from your notes, with square brackets wherever a fact is still unknown.
- Guest information, such as how to get there, what to wear, and what time food is served, written from a fact sheet you own.
- After the event: thank-you notes, survey questions and a debrief summary from your own notes.
Each of these produces questions, structure or wording. None needs ChatGPT to know something about the world that you can't verify yourself. Use it for shape and wording, and bring the facts yourself. For a scoring method to decide which tasks to hand over first, see which event planning tasks to hand to AI first, and for budgets and timings in depth, how event planners use AI to build budgets and run sheets.
Eight kinds of invented fact, and who confirms each
The mistakes cluster. These are the eight places a planner gets caught, with the person or document that settles each one.
| Kind of fact | How it goes wrong | Who confirms it |
|---|---|---|
| Capacity | One number for a room, when real capacity depends on layout (rounds, theatre rows, standing) and on the venue's safety limit | The venue, in writing, for your layout, plus any certificate it holds |
| Licences and permits: alcohol, music, entertainment, temporary structures, food stalls, road closures, fireworks | Plausible rules from the wrong place or an old year, or "none needed" | The venue's licence conditions, and the body that issues each permit where the event is held |
| Prices and fees: hire, minimum spend, service charge, corkage, overtime | "Typical" figures presented as quotes | A written quote that says what is included and when it expires |
| Availability, opening hours, curfews | Read from a cached or third-party page | The venue's calendar and a dated email |
| Logistics and timings: travel, load-in windows, parking, sound limits, sunset | Averages stated as specifics | A site visit and the venue's access sheet |
| Dietary, allergen and faith requirements | A menu described as nut-free, halal or vegan with no basis | The caterer's written allergen information, never the model |
| Insurance and contract terms | Invented standard clauses and cover limits | The contract, the insurer, and a solicitor when the stakes warrant it |
| Vendors themselves | Plausible supplier names, websites and phone numbers that don't exist or belong to someone else | The supplier's own site and a phone call |
Capacity deserves its own warning, because the plan hangs on it. With illustrative figures, the same room might hold 120 at banquet rounds, 180 in theatre rows and 250 standing, and the legal limit could sit below all of them. A model that says "holds up to 200" hasn't told you which layout it means, or who set the limit. Contract terms get the same treatment: for the wording of deposits, cancellation and damage, drafting and checking an event venue contract with AI shows what to hand over and what to verify.
A calibration test: ask about venues you already know
Instead of trusting or distrusting the tool in general, measure it on ground you know. The test takes 20 minutes.
- Pick three venues you have worked in and know well.
- In a fresh chat with no pasted facts, ask the same ten questions about each: seated capacity, standing capacity, finish time, alcohol licence in place, typical hire fee, parking, step-free access, kitchen and catering rules, noise limit, load-in access.
- Mark each answer right, wrong, or "can't tell" when you can't confirm it from what you know.
Here is how a scored sheet might look. The marks are illustrative, and yours will differ, which is the point of running it.
| Question | Venue A | Venue B | Venue C |
|---|---|---|---|
| Seated capacity | Wrong | Right | Wrong |
| Standing capacity | Wrong | Wrong | Can't tell |
| Finish time | Can't tell | Wrong | Wrong |
| Alcohol licence in place | Right | Can't tell | Can't tell |
| Typical hire fee | Wrong | Wrong | Wrong |
| Parking | Right | Can't tell | Right |
| Step-free access | Right | Right | Can't tell |
| Kitchen and catering rules | Right | Right | Right |
| Noise limit | Can't tell | Can't tell | Wrong |
| Load-in access | Can't tell | Wrong | Right |
| Totals (right / wrong / can't tell) | 4 / 3 / 3 | 3 / 4 / 3 | 3 / 4 / 3 |
Across the 30 answers that is 10 right, 11 wrong and 9 that couldn't be checked. The pattern is more useful than the total. The wrong answers cluster on numbers: capacities, fees, finish times and noise limits. The right ones are general facilities that most venues of that type share. The "can't tell" answers are the dangerous ones, because they read exactly like the right ones. Turn what you find into your office rule: a number is never accepted from the model, and a general fact is accepted only if a quick check would be cheap.
Three venues for 120 guests, asked two ways
A client wants a seated dinner for 120 on a Friday, a hard stop at 23:30, and $22,000 all in. You have three candidate venues: a converted mill (A), a hotel ballroom (B) and a garden marquee site (C). All figures here are invented.
First pass: no facts supplied. You ask ChatGPT to compare the three venues for that dinner. It returns a confident table. Set against the documents the venues later sent, the first-pass answers look like this:
| Item | Venue A: model said | A: document says | Venue B: model said | B: document says | Venue C: model said | C: document says |
|---|---|---|---|---|---|---|
| Seated capacity | 150 | 130 | 200 | 160 | 180 | 140 |
| Room fee | about $3,000 | $4,200 (17:00 to 23:00) | included with catering | waived above a $13,000 minimum spend | about $4,000 | $5,500 site fee |
| Catering per head | $65 to $75 | $78, plus 12% service | $85 to $95 | not stated | $70 to $80 | not stated |
| Finish | midnight | music ends 23:00 | 01:00 | 00:00, bar closes 23:30 | 23:00 | not stated |
Of 12 cells, 8 were wrong, 1 was partly right and 3 couldn't be checked against the documents. Nothing in the first-pass table signalled which was which. Now follow the money for Venue A. The model's figures give $3,000 for the room plus 120 guests at about $70 a head ($8,400), roughly $11,400. The venue's own figures give $9,360 for catering (120 x $78), $1,123.20 in service charge (12%), $4,200 for the room and $350 for an extra hour to cover the 23:30 finish, which is $15,033.20. A planner who quoted the client from the first pass would be $3,633 short on this venue alone.
Second pass: the venues' own text, pasted in. You paste each venue's brochure or email and use a prompt that forbids estimating:
Compare the three venues below for a seated dinner of 120 guests with a
23:30 hard stop. Use only the text I have pasted.
For every figure, give the venue and quote the phrase it came from.
If the text does not say, write NOT STATED. Do not estimate, round or
fill gaps. Do not do arithmetic; list the components and I will sum them.
[Venue A text]
[Venue B text]
[Venue C text]Now every cell carries a quotation, and the three that the documents never answered (Venue B's menu price, Venue C's menu price and Venue C's finish time) say NOT STATED instead of a plausible guess. Those cells become the questions for your next emails. Note the instruction not to do arithmetic: models can slip on long sums, or leave out a conditional charge such as the extra hour, so the components go into a spreadsheet where the formulas are visible. AI for meeting planners: venue shortlists and RFP replies goes further on writing the request that brings comparable answers back.
A run sheet where every time names its source
A run sheet mixes two kinds of line: times that someone else has fixed, and times you or the model have guessed. Suppliers can't tell which is which unless the sheet says so. This version, for Venue A, adds a source column:
| Time | Item | Source of the time |
|---|---|---|
| 17:00 | Room access; florist and AV set-up begin | Venue (fixed: hire starts 17:00) |
| 18:30 | Doors and drinks reception | Client (fixed) |
| 19:30 | Guests seated; welcome | My estimate |
| 19:45 | Starter served | Caterer (to confirm) |
| 20:30 | Main course | AI suggestion (check) |
| 21:15 | Three short speeches | Client (order); length is my estimate |
| 21:45 | Dessert and coffee | AI suggestion (check) |
| 22:15 | Music and dancing begin | AI suggestion (check) |
| 23:00 | Music ends | Venue (fixed) |
| 23:30 | Last guests leave | Client (fixed) |
| 00:00 | Room cleared; hire ends | Venue (extra hour, $350) |
Five rows are fixed by the venue or the client. Six are hypotheses that the caterer or you must confirm. In this illustration the model's first draft had music starting at 21:30, which would have run into the speeches, and allowed 20 minutes between courses, which a caterer serving 120 plated meals might not manage. Both were plausible, and neither was a fact. With the source column, the caterer sees at a glance that 20:30 is a suggestion they can push back on, while 23:00 is not.
Vendor emails: let it write, never let it assert
The invention risk in emails is the model writing what a supplier has agreed. Suppose your notes say: caterer for the Venue A dinner, 120 guests, three courses, 6 vegan, 4 gluten-free, 1 serious nut allergy, service at 19:45, access from 17:00, need written allergen information per dish, ask how the service charge applies, ask for the deadline for final numbers. A typical first draft, illustrative:
Hi [name], thank you for the wonderful tasting and for confirming that your quote includes unlimited soft drinks and a nut-free kitchen. We're delighted to confirm 120 guests for the dinner...
There was no tasting. Nobody confirmed the soft drinks. And "a nut-free kitchen" is an allergen claim that, if it reached the client, could hurt someone. The corrected email contains only facts from the notes, questions, and brackets for unknowns:
Hi [name], we're planning the dinner at [venue] for 120 guests, with access from 17:00 and the starter served at 19:45. Dietary needs so far: 6 vegan, 4 gluten-free and 1 serious nut allergy. Could you send: (1) written allergen information for every dish, and your procedure for preparing the nut-allergy meal; (2) how the 12% service charge is applied; (3) your deadline for final numbers? Please also confirm exactly what the per-head price of $78 includes.
The rule that produces the second version: an email may contain facts from your notes, questions, and placeholders. Anything that says what a supplier has agreed is written by you, from the signed quote. If you want more templates in this style, ChatGPT prompts for wedding planners: vendor emails and FAQs uses a master fact sheet that feeds every prompt.
Guest information: the facts attendees will act on
Guest-facing text feels low-risk, which is why invented facts survive in it. Ask a model for a "how to get here" page and it produces reassuring specifics: parking, the walk from the station, whether the entrance is step-free. Attendees act on every one of them.
Suppose your fact sheet says: doors at 18:30; coach drop-off at the rear car park; 40 parking spaces, pay at the machine; step-free entrance at the side door, ring the bell; dress code smart casual; dinner at 19:45; the client's last coach leaves at 23:45. Given that sheet and the grounding prompt below, the model writes a clear page and stops. Given only the venue's name, the same model tends to add "plenty of free parking", "a short walk from the station" and "the main entrance is fully accessible". The last one is the sentence that could leave a wheelchair user at the wrong door in the rain.
The rule for this kind of text is stricter than for internal paperwork. Anything about access, parking, transport or safety is copied from the venue's own information sheet and sent back to the venue for sign-off before guests see it. Outdoor events add a second trap: facts that depend on the date and the place, such as sunset or the chance of rain. The model gives a typical figure. Look up the real one for your date and put the source on the run sheet.
A grounding prompt and a five-minute fact pass
Two habits catch most of what the calibration test shows. The first goes in the prompt. Add this to anything that mixes facts and writing:
Answer only from the text I have pasted. After every figure, name the
document it came from in brackets. If the text does not say, write
NOT STATED. Do not estimate. At the end, list every assumption you made.The second is a pass you run on the output before it leaves your desk:
- Copy the draft into a document.
- Highlight every number, time, proper noun and rule.
- Beside each highlight, write its source and the date of that source.
- Any highlight without a source becomes a question to the venue or supplier, or gets deleted.
- Record "confirmed by [person] on [date]" in your own file, not in the model's chat.
In an illustrative 400-word proposal for the Venue A dinner, such a pass turns up three unsourced highlights: "seats 200", "bar open until 01:00" and "licence in place". Each becomes a question rather than a claim. Five minutes against a proposal that goes to a client is cheap.
Search and deep research help at the edges. With search switched on, ChatGPT can cite web pages, and deep research (with a usage allowance that varies by plan) can read uploaded files as well as the web. Use them to find the page to check, then open the page yourself.
Keep an invention log so the rules sharpen
Every invented fact you catch is data about where your own process leaks. Keep a one-line log, in a shared sheet, for each one. An illustrative month:
| When | Where it appeared | What was invented | Kind | Caught by |
|---|---|---|---|---|
| Week 1 | Venue table | Seated capacity 200 (document: 160) | Capacity | Venue brochure |
| Week 2 | Guest page | "Free parking for all guests" (40 paid spaces) | Logistics | Venue information sheet |
| Week 2 | Caterer email | "Nut-free kitchen" | Allergen | Caterer's allergen sheet |
| Week 3 | Run sheet | 20 minutes between courses | Timing | Call with the caterer |
| Week 4 | Proposal | "No permit needed for a marquee" | Licence | Venue licence conditions |
After a month or two the log tells you which of the eight kinds dominates for you, and that is where the fact pass should be strictest. It also gives you something to show a new assistant on day one, which beats a general warning to "be careful with AI". If an entry reached a client or a supplier before it was caught, write that down as well and change the step that let it through, whether that was a missing source column or a skipped read-through.
Three beliefs that don't survive a check
"Switch on search and the facts are verified." A citation proves the model read a page, not that the page is right. A directory entry may predate the venue's refurbishment. Open the source, check who wrote it and when, then confirm by email.
"Ask 'are you sure?' and it will check itself." Asking again re-runs the same guesser. It may flip to agree with your doubt or restate the invention in firmer words. The independent check is a document from the venue.
"A newer model won't do this." Model versions change often, and OpenAI's help page and terms still warn that output may be inaccurate. Run the calibration test on the model you use, and run it again when the model changes. If you want ChatGPT to handle the repeatable parts of your workflow without ever holding facts, the planning checklists in building an event planning checklist per event type with AI are a safe place to start.
Questions planners ask about ChatGPT and facts
Can ChatGPT check whether my event needs a licence?
It can list the kinds of permission worth asking about, such as alcohol, music, temporary structures, food stalls and road closures. It can't know the rules where your event is held, or how a particular venue's own licence is worded. Ask the venue for its licence conditions in writing and confirm each permit with the body that issues it.
Is ChatGPT's web search enough to verify venue details?
No. Search lets it cite pages, but the page may be an old directory entry or a third-party listing. Open every citation, check who wrote it and when, and then confirm capacities, fees and dates with the venue by email. Treat search as a way to find the right page to check, not as the check itself.
Which numbers is ChatGPT most likely to get wrong?
The ones that vary by venue and by day: capacities by layout, hire fees, minimum spends, finish times, noise limits and travel times. It tends to give typical figures as if they were quotes. Generic facts, such as whether a venue type usually has a kitchen, are safer. Run the calibration test in this tutorial on venues you know.
Is it safe to paste a client's guest list into ChatGPT?
Not as it stands. Guest lists hold names, contact details and often dietary or access needs, which can reveal health or religion. Paste counts and categories instead, such as 6 vegan, 4 gluten-free, 2 step-free. If you need the model to work on real names, use a business plan with admin controls and ask your data-protection adviser first.
Does a newer ChatGPT model stop it inventing facts?
Not reliably. Model versions change often, and OpenAI's own help page and terms still say output may be inaccurate and shouldn't be the sole source of truth. Newer models may make fewer slips, but you can't tell which cells are wrong by reading them. Re-run the calibration test whenever the model changes.
Further reads
- Event Planner Intake Form: Capture Every Detail Before You Quote — Capture the facts you need before any quote or comparison.
- A Wedding Planner's AI Workflow From Enquiry to Final Timeline — A planner's AI workflow from enquiry to final timeline.
- ChatGPT Meeting Minutes Generator: A Client-Call Recap for Event Planners — Turn a client call into a recap without inventing decisions.
- Best AI Tools for Small Event Planning Businesses — Tools beyond ChatGPT for a small planning business.
- Event Planning Using AI After the Day: Feedback, Sponsor Reports, Debrief — Use AI after the day for feedback and sponsor reports.
- AI for Event Venues: From Enquiry to Quote Without Missing Details — The venue's side: enquiry to quote without missing details.
- AI Software for Event Planning: What HoneyBook AI Covers — A job-by-job map of what HoneyBook AI handles for event planners, what ChatGPT does better and what stays with a person.
- Event Sponsorship Proposal: Tiers and Pitch Emails Drafted With AI — Price four sponsor tiers from your budget gap, then have AI draft the proposal and a four-email pitch sequence. One worked event, with the sums.
- AI Tools and AI Development: The Complete 2026 Guide — the AI hub, including every tutorial in the AI-for-business series.
Sources: OpenAI help articles 'Does ChatGPT tell the truth?' and Deep research FAQ, and OpenAI Terms of Use (accuracy section). Venue names, figures and quotes in the examples are invented for illustration.