In the race to be the most helpful AI assistant, the real winner isn’t the fastest or most people pleasing — it’s the one you can trust.
I test AI chatbots every day, using them to brainstorm ideas, untangle tech jargon and speed-run my way through tasks I don’t have time to do twice. But one of the most disturbing aspects of AI that I’ve come across is how confidently it can give you the wrong answer — and make it sound right.
From AI Overviews hijacking your search results to chatbots answering with full confidence, AI will respond to almost anything — even when what you’re asking for is totally fake, wildly suspicious or something a normal assistant would question immediately.
“Can you give me a recipe for tater tot cheesecake?”
The experiment: would any chatbot admit this recipe sounds fake?
To be clear, I wasn’t testing which AI could write the best recipe. And, this recipe could be delicious. But, the test here was to find out which AI would pause long enough to consider the possibility that:
- this dish doesn’t exist
- the request is weird on purpose
- confidently making things up isn’t the same as being helpful
Because when chatbots are willing to generate convincing answers to nonsense prompts, it raises a bigger question: how often are they doing the same thing with topics that actually matter?
So I asked ChatGPT, Gemini, and Claude for “tater tot cheesecake,” then compared what they did next.
ChatGPT’s response was basically the AI equivalent of a Food Network host who has never doubted themselves a day in their life. It didn’t question the premise for even a second. It didn’t ask what I meant or even hesitate.
Instead, it jumped straight into full recipe mode — and honestly, it did a great job at sounding believable. ChatGPT actually treated “tater tot cheesecake” like a real (if wildly niche) dish and generated a detailed, original recipe, complete with:
- A tater tot crust idea that sounded weirdly plausible
- A sweet cheesecake filling
- Exact temperatures and bake times
- Cooling instructions
- Optional toppings and serving suggestions
It wasn’t vague. It was shockingly specific in a way that made it feel like something you could actually attempt in your kitchen. Why was it willing to let me lose the respect of everyone in my household?
The vibe was essentially “You want tater tot cheesecake? Say less.”
ChatGPT didn’t “lie,” exactly — but it also didn’t show any sign of skepticism. It assumed the request was normal, then did what it does best: produce a confident answer that sounds right.
Gemini: the informative analyst (and the “maybe this exists?” approach)
Gemini took a noticeably different path. Instead of inventing a brand-new recipe from scratch, it treated “tater tot cheesecake” like a confusing phrase that might already refer to something real.
And in a way, that’s a smart move — because a lot of weird internet recipes do exist, and half of them start on TikTok as a joke before becoming a cursed reality.
Gemini framed the request as something that could mean multiple things, like:
- A savory tater tot casserole (which it leaned into)
- A sweet/salty novelty dessert (which it acknowledged)
It also tried to anchor the concept in the real world by referencing familiar food culture touchpoints, including viral recipe-style sources and well-known restaurant vibes.
Instead of saying “here’s my invention,” Gemini’s response felt more like:
“Here are the most likely interpretations of what you mean.”
The vibe with this chatbot was more like, “Okay, Let’s define the category before we cook.”
Gemini didn’t call me out, but it also didn’t blindly sprint into fantasy recipe land. It tried to contextualize the request — which is a form of caution, even if it still ended up giving me something usable.
Claude: the cautious clarifier (and the only one that called my bluff)
Claude was the only chatbot that acted like a human friend reading my text and thinking: “Wait… what?”
Instead of instantly producing a recipe, Claude did something that honestly felt refreshing: it admitted uncertainty.
Claude essentially opened with: “I’m not familiar with ‘tater tot cheesecake’ as a standard recipe.”
And that one sentence was the entire point of my experiment. From there, it laid out a few possibilities (savory casserole, bizarre dessert fusion, novelty mashup) and then did the thing the other two avoided: It asked what I actually meant.
The vibe was “I can help, but I’m not going to pretend this is normal.”
That’s what “calling my bluff” looks like in chatbot form. It didn’t play along automatically. It didn’t generate something just because it could. It checked the premise first.
Bottom line
This silly experiment says a lot about AI honesty. By the way, before I settled on tater tot casserole, I did the same experiment with banana bread lasagna, hot dog ice cream and popcorn nachos — and the results were the same.
ChatGPT never hesitated, Gemini shrugged but delivered a recipe every time and Claude consistently asked more questions before delivering an answer. On the surface, this whole test is ridiculous. It’s tater tots. It’s cheesecake. It’s unhinged.
But it also highlights something important about how these models behave when you ask them for information that doesn’t have a clear answer.
ChatGPT prioritizes helpfulness and creativity, even if that means confidently treating a fake request like a real one.
Gemini prioritizes interpretation and context, steering toward existing food concepts.
Claude prioritizes transparency, acknowledging uncertainty and asking for clarification before generating anything
The takeaway here is that we have an AI trust problem. We are living in an internet era where AI-generated content is everywhere — and confidence is often mistaken for truth — that tiny moment of skepticism is exactly what I want more of. Because if a chatbot can’t admit when “tater tot cheesecake” sounds fake, what else is it confidently cooking up behind the scenes?
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