When we're excited about an idea, we often see only its potential. But the best ideas are those that have been stress-tested against skepticism. Today's prompt template turns your AI into a constructive critic who can spot the cracks in your favorite plans before they become costly mistakes.
The Task
Transform your AI assistant into a thoughtful devil's advocate. This isn't about pessimism—it's about uncovering blind spots, questioning assumptions, and identifying risks you might have overlooked in your enthusiasm.
The Prompt Template:
I'd like you to act as a devil's advocate for my idea/plan below. Please:
1. Identify 3-5 potential risks or failure points I may not have considered
2. Challenge any unstated assumptions I'm making
3. Point out any logical flaws or inconsistencies in my reasoning
4. Suggest what could go wrong even if everything goes "right"
5. Highlight any important stakeholders or perspectives I'm overlooking
Be constructive but thorough—I want to strengthen this idea by understanding its weaknesses.
My idea/plan: [INSERT YOUR IDEA HERE]Sample Problem
Let's say you're planning to launch a subscription-based meal planning app that uses AI to create personalized recipes based on users' health goals, dietary restrictions, and local grocery store inventory.
Solution
Prompt
I'm planning to launch a subscription-based meal planning app that uses AI to create personalized recipes based on users' health goals, dietary restrictions, and local grocery store inventory. My market research shows strong demand - 73% of surveyed users said they'd pay $9.99/month for this service. I've secured partnerships with major grocery chains, my AI algorithm has been tested with impressive accuracy rates, and early beta users are raving about how much time and money they're saving. The health and wellness market is booming, meal kit services are proving people will pay for convenience, and my unique AI personalization angle sets me apart from generic meal planning apps. Everything points to this being a slam dunk.
My team is energized, investors are interested, and I'm ready to quit my day job to go all-in on this venture. The launch timeline is aggressive but achievable, and I've already started building buzz on social media. I can't see what could possibly go wrong with such a well-researched, timely concept that solves a real problem people face every day.
Can you play devil's advocate and tell me what's wrong with my plan? What am I missing?
Gemini 2.5 Pro
Of course, I can play devil's advocate. While your concept for an AI-powered meal planning app is built on a foundation of strong market research and promising initial feedback, a healthy dose of skepticism is crucial for any new venture. Here’s a look at what could go wrong and what you might be missing:
Market & Competition: A Crowded and Fickle Space
Your market research indicates a strong demand, but the app market, particularly for health and wellness, is notoriously saturated. While your AI angle is a differentiator, you're not just competing with other meal planning apps, but also with free recipe websites, social media food influencers, and established weight loss programs that are increasingly incorporating technology.
The "Good Enough" Factor: Many people find "good enough" solutions for free. They might use a combination of Google searches, Pinterest boards, and their existing knowledge to plan meals. Your app needs to be demonstrably better to convince users to pay a recurring fee.
Subscription Fatigue is Real: Consumers are becoming increasingly selective about their subscriptions. With streaming services, other app subscriptions, and monthly bills, another $9.99 charge might be the first to go when budgets tighten. You're not just competing for market share; you're competing for a line item on your users' monthly credit card statements.
Business Model & Financials: The Perils of a $9.99 Price Point
While 73% of surveyed users said they'd pay $9.99/month, what people say they'll do and what they actually do can be vastly different. The path from a positive survey response to a user entering their credit card information is fraught with friction.
Customer Acquisition Cost (CAC): Acquiring a new app user can be expensive. Estimates for 2024 show that the cost per install (CPI) can range from $1.50 to over $6.00, depending on the platform and user demographics. Your customer acquisition cost, which includes marketing and sales expenses, will be even higher. If your CAC approaches or exceeds a few months of subscription revenue, your business model could quickly become unsustainable.
High Churn Rate: The subscription app world is plagued by high churn rates. It's not uncommon for apps to lose a significant percentage of their users within the first few months. If your app doesn't become an indispensable part of a user's daily or weekly routine, they are likely to cancel after the initial excitement wears off. A high churn rate means you're constantly spending to replace lost users, a costly endeavor.
Hidden Costs: Beyond the obvious development and marketing costs, you need to factor in:
Payment Processing Fees: A percentage of every transaction will go to the payment gateway.
Server and API Costs: Maintaining a robust backend, especially with a sophisticated AI, and constantly pulling real-time data from grocery stores can be expensive.
Customer Support: As your user base grows, so will the need for a responsive and helpful customer support team.
Content and Algorithm Updates: Your recipe database will need constant refreshing, and your AI algorithm will require ongoing maintenance and improvement to stay "impressive."
Technology & Operations: The Double-Edged Sword of AI and Partnerships
Your AI's "impressive accuracy" in a tested environment is a great start, but the real world is messy.
Maintaining AI Accuracy:
The Cold Start Problem: How will your AI perform for a brand-new user with no data? Initial recommendations are critical for user retention.
Evolving Tastes and Health Goals: A user's preferences and health needs will change over time. Your AI needs to be sophisticated enough to adapt and not get stuck in a feedback loop of similar recommendations.
Garbage In, Garbage Out: The accuracy of your AI is heavily dependent on the quality of the data it receives. Inaccurate or incomplete grocery store inventory data can lead to frustrating user experiences. What happens when a user is sent to a store for an ingredient that's out of stock? They'll blame your app, not the grocery store.
The Challenge of Grocery Store Partnerships:
Real-Time Data Integration: Maintaining seamless, real-time API integrations with multiple grocery chains is a significant technical and logistical challenge. Each partner will have its own system, and downtime or inaccuracies on their end will directly impact your user experience.
Scalability: As you expand to new regions and partner with more stores, the complexity of managing these integrations will grow exponentially.
User Acquisition & Retention: The Long Grind Beyond the Launch Buzz
Building initial buzz is one thing; sustaining growth and retaining users is another challenge entirely.
The Onboarding Hurdle: Your app's success hinges on users seeing its value quickly. A complicated or lengthy onboarding process will lead to a high drop-off rate. How will you guide new users to input their health goals, dietary restrictions, and local store preferences without overwhelming them?
Beyond Personalization: The Need for Community: While personalization is a strong feature, it can also be an isolating experience. Successful apps often foster a sense of community. How will you connect your users with each other? Consider features like:
User-generated recipe ratings and reviews.
Shared meal plans or cooking challenges.
Forums or discussion groups around specific dietary needs or health goals.
The "What's Next?" Factor: After a user has been with you for a few months, what will keep them engaged? You need a long-term content and feature roadmap to prevent the app from feeling stale. This could include new recipe collections, seasonal eating guides, or integrations with fitness trackers.
In conclusion, while your concept is strong, your success is not a foregone conclusion. Be prepared for a marathon, not a sprint. The key will be to execute flawlessly on the user experience, manage your costs diligently, and continuously innovate to stay ahead of the competition and keep your users engaged long after the initial download.
ChatGPT 4o
You're clearly passionate and have laid a strong foundation for a promising business—but since you asked for a devil’s advocate perspective, here’s a hard-nosed look at what could go wrong, broken down by key risk areas:
🧠 Cognitive Biases at Play
Before diving into specifics, consider this: your enthusiasm may be hiding blind spots. This plan sounds airtight because you’ve validated much of it—but confirmation bias and optimism bias could be leading you to overweigh positives and discount negatives.
⚠️ Product & Market Risks
1. Overestimated Willingness to Pay
73% said they would pay $9.99/mo, but real behavior ≠ survey intent.
Freemium fatigue is real. Consumers cancel subscriptions quickly when value isn’t instantly felt.
Your app’s core value must be delivered in the first 3 days, or churn spikes.
2. “AI Personalization” Isn't Always Magical
If recommendations occasionally miss the mark (e.g., ingredients someone hates or can’t find), users may lose trust fast.
Explainability and control are key: users need to feel they’re in charge, not at the mercy of an algorithm.
3. You’re Competing with Free
Countless free or $1.99 meal planning tools (Paprika, Whisk, Pinterest boards).
To succeed at $9.99/month, you must deliver exponentially more value, not just better UX.
🏗️ Operational Challenges
4. Integration with Grocery Stores Could Break
Inventory data can be inaccurate, delayed, or siloed.
What if a partner changes their API policy, adds fees, or pulls access?
Regional differences (brands, stock levels) might break personalization in unexpected ways.
5. Scaling Personalization Is Hard
What works with 100 beta users might collapse at 10,000. Personalization and inventory matching are compute- and logic-intensive.
Grocery stores are notoriously fragmented: localized inventory across thousands of stores makes nationwide scaling very complex.
📉 Retention & Business Model Risks
6. Users Don’t Use Meal Planners Forever
Even if users love the app, many see it as a temporary solution—until they learn to eat healthier or fall back into habits.
Your LTV may be lower than expected unless you build strong retention loops (e.g., social sharing, gamification, evolving plans).
7. Subscriptions Are a Treadmill
Your revenue model depends on low churn + high acquisition.
CAC could quickly outstrip LTV if your paid growth channels don’t scale or become expensive.
Seasonal churn is likely (e.g., New Year’s resolution spikes, then drops by March).
👥 Competitive Pressures
8. Entrenched Players Could Copy You
Instacart, HelloFresh, MyFitnessPal, or even OpenAI-powered competitors could replicate your AI approach quickly.
They already have users, data, and distribution channels.
9. AI-First is Becoming the Norm
The "AI-powered" edge is shrinking fast—everyone from startups to Google is adding AI features.
If your app feels like a “thin wrapper over GPT,” users may churn fast or use ChatGPT directly with a few good prompts.
💼 Personal Risk
10. Quitting Your Day Job
This is likely your biggest personal gamble.
If launch delays, fundraising slows, or early growth plateaus, you could be caught in a financial crunch before PMF is clear.
✅ Constructive Final Thoughts
All of this doesn’t mean don’t do it. It just means:
Stress-test retention before launch—assume lower conversion and higher churn than your current data suggests.
Build strong onboarding, feedback loops, and a free-to-paid funnel to ease CAC pressure.
Have a contingency plan if grocery APIs flake or investor timelines slip.
Don’t make AI the product—make the outcome (saved time, healthier eating) the hero.
If you can survive this kind of scrutiny, then you’re on your way to something meaningful—and investors will see that, too.
Would you like help pressure-testing your go-to-market strategy, retention loops, or pitch deck next?
Claude Sonnet 4
Here are the potential pitfalls and blind spots in your meal planning app venture:
Market Reality vs. Survey Data Your 73% interest rate is likely inflated by survey bias - people overestimate their willingness to pay when there's no actual transaction involved. The gap between "I'd pay for this" and actually pulling out a credit card is enormous. Most successful subscription services see conversion rates of 2-5% from interested prospects to paying customers.
Competitive Moats Are Weaker Than They Appear Your grocery partnerships and AI algorithm sound impressive, but these advantages may be temporary. Large tech companies (Google, Amazon, Apple) could easily replicate your features with superior resources. Existing players like MyFitnessPal or Lose It! could add meal planning features overnight. Your "unique" positioning might last months, not years.
The Subscription Graveyard The subscription market is brutally competitive with incredibly high churn rates. Even satisfied customers cancel subscriptions during budget tightening. Your $9.99 price point puts you in direct competition with Netflix, Spotify, and other entertainment subscriptions that people prioritize over utility apps.
Operational Complexity You Haven't Mentioned Maintaining real-time grocery inventory data across multiple chains is a logistical nightmare. Stores frequently run out of items, prices fluctuate, and product availability varies by location. One bad experience with "personalized" recipes calling for unavailable ingredients will tank user satisfaction.
The Behavior Change Challenge Your app requires users to fundamentally change how they shop and cook. Most people have deeply ingrained grocery habits and won't easily adapt to AI-generated lists, even if they're theoretically better. The friction of learning new recipes and changing shopping routines is substantial.
Hidden Costs and Resource Drain Customer acquisition costs for apps typically run $20-50+ per user. You'll need significant marketing spend to overcome the natural skepticism around food AI. Customer support for dietary restrictions, allergies, and recipe failures will be resource-intensive. Recipe development and testing at scale requires ongoing investment.
The "Quit Your Day Job" Risk Going all-in before validating actual paying customers (not just beta testers) is premature. Beta users who don't pay aren't real customers - they're often just enjoying free access. The psychological and financial pressure of having no backup plan often leads to poor decision-making when challenges arise.
Your enthusiasm is valuable, but consider a more measured approach: validate with paying customers first, maintain your income stream longer, and build stronger competitive moats before the big players notice your success.
Extra Credit
Take the critique one step further by asking your AI to:
Rank the identified risks by likelihood and impact
Suggest specific mitigation strategies for the top 3 risks
Propose alternative approaches that might avoid these pitfalls entirely
Try this prompt: "Based on your critique, what would be the 'steel man' version of this idea—the strongest possible version that addresses these concerns?"
General Takeaways
The devil's advocate approach works because it:
Normalizes criticism: It's easier to hear tough feedback when you've explicitly asked for it
Catches expensive mistakes early: Better to find flaws in the planning stage than after launch
Improves decision-making: Understanding downsides helps you make informed choices, not just hopeful ones
Builds resilience: Ideas that survive scrutiny are more likely to succeed in reality
Remember: The goal isn't to kill your ideas—it's to make them bulletproof. Use this template whenever you need to move from "this could work" to "here's exactly how this will work, despite these specific challenges."