Prompts that make ChatGPT or Claude try to kill your app idea

Why your AI says your idea is great

Chat assistants tend to agree with the person asking. The evidence runs from lab studies to a vendor rollback to builder threads.

• The research. Sharma et al. (2023) tested five AI assistants on four kinds of free-text tasks and found they tend to match the user's stated beliefs over the truthful answer. In the human preference data used to train assistants, answers that matched the user's views were more likely to be preferred. The authors name those human judgments as one likely cause. A 2025 study by Cheng et al. tested 11 models on requests for advice and found they affirmed users' actions 50% more than humans did.^{[1], [12]}
• A public example. In a post dated 2025-04-29, OpenAI said it had removed a GPT-4o update from ChatGPT because the update was "overly flattering or agreeable". That release is gone. The pattern Sharma et al. describe is general to assistants, so a prompt still has to push against it.^{[1], [2], [3]}
• What builders report. In a March 2026 r/vibecoding thread on knowing what to build, one reply describes two weeks of building after ChatGPT, Claude and Replit all praised the idea. A June 2026 thread asks whether AI validation only tells you what you want to hear, and another tells builders to ask AI to kill their app.^{[4], [5], [6]}
• What builders say about paying for validation. A July 2026 r/vibecoding post pitched a one-off AI validation report with sourced competitors, demand signals and a section on what would kill the idea. Many replies said they would just ask ChatGPT. The prompts below ask ChatGPT or Claude for those same three things.^[7]

Rules that make a validation prompt useful

Our rules (editorial). Where research backs a rule, it is cited.

1. 1
   Name the buyer before the features. Name a role and a situation, such as "office managers at dental clinics with 5 to 20 staff". "Small businesses" gets you a generic answer, and a feature list tells the model nothing about demand.
2. 2
   Ask for the case against, and say praise is not wanted. Assistants lean toward the view the user brings. Tell the model its job is to find the reasons the idea fails, and that summaries and encouragement are not wanted.^[1]
3. 3
   Start a fresh chat with memory off, and keep your opinion out. Earlier messages where you pitched the idea stay in that chat, and memory can carry them into new ones. In Claude, memory is on by default on Free, Pro and Max plans (as of October 2026): switch Memory off in the + menu before your first message, or use an incognito chat. In ChatGPT, start a Temporary Chat and choose Unpersonalized before your first message; a Personalized one can still use your memories. Do not write "I think this is a great idea" in the prompt either: assistants tend to go along with the beliefs the user states.^{[1], [13], [14]}
4. 4
   Make it label every claim. Each claim is either "sourced", with a link the model opened, or "assumption". Without web search, everything is an assumption, and the answer should say so. Check the links anyway. In a 2023 study of short literature reviews ChatGPT wrote, 55% of GPT-3.5 references and 18% of GPT-4 references were to papers that do not exist. Search does not make links safe: in a 2025 test where eight AI search tools had to find and link the news article an excerpt came from, more than half of Gemini and Grok 3 answers cited fabricated or broken URLs.^{[8], [11]}
5. 5
   Force one verdict from a fixed set. Cook it, sleep on it or kill it, with "it depends" ruled out. An open question lets the model list pros and cons and stop there. A forced choice makes it commit, and you can argue with a commitment.
6. 6
   Turn on web search for competitors and complaints. Without it, the model answers from training data that stops at a cutoff date, and it cannot see this year's pricing pages or threads.
7. 7
   Run it twice. Paste the same prompt into two new chats. Output varies from run to run: Anthropic's API docs say results are not fully deterministic even at temperature 0.0. If the verdict flips between runs, the evidence is thin.^[9]

The prompts: copy, fill the brackets, paste

Start with prompt 1. Prompts 2 and 6 need web search turned on, or the model answers from its training data. For prompt 4, copy model prices from the provider's own pricing page, or see the cost-per-call table, and take the payment fee from your payment processor's pricing page. Use a fresh chat with memory off for each prompt (rule 3).

I am deciding whether to build an app. Your job is to find the reasons it will fail. Do not encourage me, do not summarize my idea back to me, and do not give general startup advice.

The idea: [one sentence on what it does]
Who pays: [a role and a situation, such as "office managers at dental clinics with 5 to 20 staff"]
What they use today: [a tool, a spreadsheet, a person, or nothing]
My price: [amount and billing period, or "not decided"]
What I bring: [skills, access or audience that matter here, or "nothing special"]

Rules:
1. Label every factual claim "sourced" with a link you opened in this chat, or "assumption". If you cannot browse, label everything "assumption" and say so at the top.
2. Never invent a company, number, quote or link. If you are not sure something exists, write "unverified".
3. Keep the whole answer under 400 words.

Answer in this order:
A. The three strongest reasons this fails, most fatal first.
B. Who already solves this, including free tools, spreadsheets and features of software the buyer already uses.
C. The single riskiest assumption, and the cheapest test that could prove it wrong within 7 days, before any code.
D. A score from 1 to 5, one line each: pain, reach (can I find and contact these buyers), willingness to pay, competition (5 means little), my advantage.
E. One verdict: COOK IT (not building would be the mistake), SLEEP ON IT (name the one test that would decide it) or KILL IT (at least one problem is fatal). Do not pick SLEEP ON IT to avoid choosing. One sentence on why.

Search the web for products that already solve this problem: [the problem, in the buyer's words], for [who].

If you cannot browse the web in this chat, reply only "No web access" and stop.

Find up to 10. Include direct competitors, general tools people bend to do this job (spreadsheets, templates, a feature inside a CRM or another app they already pay for), and any built-in feature of [the platform my buyer already uses].

Give one table row each:
- Name, and a link to its homepage
- Link to its pricing page, the cheapest paid plan shown there, and today's date. Write "no public pricing" if there is none.
- Who it is for, in under 10 words
- One complaint about it from the past 12 months (review site, forum or Reddit thread), with the link. Write "none found" if you found none.

Only list pages you opened in this chat. Do not fill any cell from memory. After the table, say in two sentences where the gap is, or say plainly that you found none.

It is 12 months from now. I built [your idea] for [who], launched it, and it failed: fewer than 10 people ever paid.

Write the post-mortem. Give the five most likely causes, ranked by likelihood. Consider at least these: the problem was not painful enough to pay for, the buyers were hard to reach, a free or existing tool was good enough, the price did not cover running costs, and I could not finish or maintain it.

For each cause give:
- the early warning sign I could have seen in the first 30 days
- a test I can run this week, before writing code, that shows whether it is already true

Leave out causes that apply to every startup. Label each claim "sourced (link)" or "assumption".

Check whether my price covers what my app costs to run. Show all arithmetic.

What one user action does: [for example, "summarizes one uploaded PDF"]
AI calls per action: [number], model: [model name]
Tokens per call: [input] in, [output] out. If I left these blank, use 2,000 in and 600 out and say that you did.
Model list price per million tokens: [input price] and [output price]. I copied these from the provider's pricing page today. If I left them blank, stop and ask me for them. Do not guess prices.
My planned price: [amount per month]
Payment fee per charge: [percent plus fixed fee, from your payment processor's pricing page]
Other monthly costs: [hosting, database, email]

1. Cost of one action = (input tokens times input price + output tokens times output price) / 1,000,000.
2. Monthly AI cost for a light user (10 actions a month), a typical user ([your guess] a month) and a heavy user (50 actions a day).
3. My margin on each, after payment fees. How many heavy users would cancel out the profit from 10 typical users?
4. One change to make before launch: a usage cap, a smaller model, a different unit to charge for, or a higher price. One paragraph, with the numbers.

Work on [your idea] for [who] in two passes.

Pass 1, the best case. Write the strongest honest argument that this will work, in under 150 words. Use only claims that could be checked: who has the pain, what they pay today, why now.

Pass 2, the attack. You have seen 50 versions of this idea and backed none of them. Take the Pass 1 argument apart point by point. For each point, say whether it rests on evidence (with a link you opened) or on hope.

Then tell me which single point from Pass 1 survived. If none did, write "none". End with one line: what I would need to hear from real buyers before writing code.

Below is an answer an AI gave me about my app idea. Audit that answer. Do not judge the idea again from scratch.

[paste the earlier answer here]

1. List every factual claim in it: market size, competitors, demand, prices, what users do.
2. Mark each one "sourced" (it gave a link, you opened it, and the page supports the claim), "unsupported" (no working source) or "wrong" (you can show it is false, with a link).
3. Quote every sentence that praises the idea without evidence.
4. Rewrite the conclusion using only the sourced claims. If none are sourced, say that no conclusion can be drawn yet.

What a prompt can and cannot check

Editorial summary, based on how chat tools work and the sources cited. "Web search" means the chat tool's own browsing, turned on.

Olune fetches data for the first three rows and has no App Store, Google Play or Chrome Web Store signal. Full list: what Olune checks and what it cannot.^{[8], [9], [10], [11]}

How to read the answer

Our checklist (editorial). Tick these off before you act on anything a prompt told you.

• Open every link. Cross out any claim whose link fails, or whose page does not say what the model says it does.
• Count the competitors yourself. Search the obvious phrase and count the products doing the job. If you find more than the model listed, it missed part of the market. Steps: the five-search check.
• Distrust round numbers. Market sizes, growth rates and user counts with no link are guesses, however precise they look.
• Check the dates. A complaint from three years ago may already be fixed. Give most weight to the past 12 months.
• Check the verdict against the reasons. If the answer names a fatal problem and still says cook it, it is being polite. Ask it to choose again using only its list of reasons it fails.
• Check that it kept your buyer. If the answer drifts to "businesses" or "users" in general, run it again with a narrower buyer.
• Run the cheapest test before any code. The riskiest assumption and its test are the most useful lines in the answer. Do that test before you open Cursor or Lovable.
• Two runs, two verdicts: treat it as sleep on it. Then go and get the evidence that would decide it.

Common questions

Sources

Each number in brackets on this page points here. Prices and limits change; the date is when we read the page.

1. [1]Sharma et al., "Towards Understanding Sycophancy in Language Models", arXiv 2310.13548 (2023, v4 2025-05-10) · accessed 13 October 2026
2. [2]OpenAI, "Sycophancy in GPT-4o: What happened and what we're doing about it" (2025-04-29) · accessed 13 October 2026
3. [3]Simon Willison, "Sycophancy in GPT-4o" (2025-04-30), on the OpenAI post · accessed 13 October 2026
4. [4]r/vibecoding, "Feels like building got easier but knowing what to build got harder" (2026-03-25) · accessed 10 October 2026
5. [5]r/vibecoding, "So this is very important ask Ai to kill your app" (2026-06-19) · accessed 13 October 2026
6. [6]r/vibecoding, "Vibe coders: how do you decide an idea is worth building BEFORE you sink 3 weeks into it?" (2026-06-07) · accessed 10 October 2026
7. [7]r/vibecoding, post pitching a one-off AI validation report, and its replies (2026-07-29) · accessed 10 October 2026
8. [8]Walters and Wilder, "Fabrication and errors in the bibliographic citations generated by ChatGPT", Scientific Reports (2023-09-07) · accessed 13 October 2026
9. [9]Anthropic API docs, Messages: create (temperature parameter) · accessed 13 October 2026
10. [10]Google Ads Help, "Use Keyword Planner" · accessed 13 October 2026
11. [11]Jaźwińska and Chandrasekar, Tow Center for Digital Journalism, "AI Search Has a Citation Problem", Columbia Journalism Review (2025-03-06) · accessed 13 October 2026
12. [12]Cheng et al., "Sycophantic AI Decreases Prosocial Intentions and Promotes Dependence", arXiv 2510.01395 (2025-10-01) · accessed 13 October 2026
13. [13]Claude Help Center, "Use Claude's chat search and memory to build on previous context" · accessed 13 October 2026
14. [14]OpenAI Help Center, "Temporary chat in ChatGPT" · accessed 13 October 2026

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