AI Disclosure: This article was co-developed with the assistance of ChatGPT (OpenAI) and finalized by Plainkoi.
Not long ago, I asked an AI to list major events from the 19th century. It gave me a detailed breakdown of “The Siege of Kensington”—dates, casualties, political aftermath.
One small problem: it never happened.
Welcome to the strange world of AI hallucinations—when models make things up and say them with a straight face. It’s not a bug. It’s part of how they work.
But here’s the good news: you can catch these errors before they make it into your notes, emails, or published work. You just need to stop treating AI like a vending machine and start using it like a panel of quirky, biased, but surprisingly useful advisors.
Let’s talk about why it helps to bring more than one voice into the room—and how doing so makes you a sharper, more strategic thinker.
Why AI Hallucinates (and What You Can Do About It)
AI doesn’t “know” facts. It doesn’t “remember” history. It just predicts the next likely word based on its training.
So when it spits out fake events, bogus citations, or imaginary experts, it’s not trying to deceive you. It’s just doing what it does best: sounding plausible.
The twist? Each AI model is trained differently. That means each one has its own blind spots, biases, and tendencies to bluff.
- One model might be polished but vague.
- Another might be factual but robotic.
- A third might be confident—and completely wrong.
Relying on a single model is like taking advice from one person and calling it research. You need multiple perspectives to spot the gaps.
Ask the Room: How Cross-Checking Exposes Hallucinations
Try this experiment: Ask three AI models the same question—say, “What caused the 2008 financial crisis?”
You might get:
- ChatGPT: a smooth, structured economic overview
- Claude: a deeper dive into ethics and systemic risk
- Gemini: up-to-date links and market-specific terminology
- Grok: a blunt, bite-sized summary with punch
If they all say the same thing, great—you’ve likely hit solid ground.
If they don’t? That’s your cue to dig deeper. The disagreement isn’t a problem—it’s a clue. You’ve just triggered what I call the Hallucination Filter.
Instead of trusting any one answer, you're triangulating truth. And in the process, you’re sharpening your own instincts.
Every Model Has a Blind Spot—Including Yours
Let’s get real: no AI model is “neutral.” Each one has its own personality:
- ChatGPT is friendly and organized—but sometimes overly cautious or generic.
- Gemini can feel current and factual—but lacks nuance or coherence at times.
- Claude is reflective and ethical—but may fudge citations.
- Grok is fast and snappy—but misses technical depth.
Here’s the kicker: the more you use one model, the more your prompts start to bend around its strengths. You adapt to its quirks without even realizing it.
That’s why switching models is so powerful. It doesn’t just give you different answers—it forces you to rethink your questions.
Pro tip: If Model A stumbles but Model B nails it, don’t just blame the AI. Look at your prompt. What changed?
Prompt Like a Polyglot: Speak Their “Language”
Each model responds better to a different communication style. Think of them like dialects:
- Claude likes longform reflection.
- ChatGPT thrives on structure and clear instruction.
- Gemini wants quick, factual asks.
- Grok prefers casual, punchy tone.
Same question, different voice—different results.
Example prompt: “Write a Python function to sort a list.”
- ChatGPT: gives you
sorted()with neat formatting. - Claude: adds thoughtful commentary on edge cases.
- Gemini: might suggest optimizations or link to docs.
You didn’t just get an answer. You got three ways to think about the problem.
Reset the Room: Why Fresh Chats Matter
Ever have an AI answer that feels weirdly off-topic? You might be running into contextual drift.
Say you’ve been chatting about sci-fi for ten messages. Then you ask, “What are the best world-building strategies?” The model might think you mean fiction, not urban planning.
This is why a clean slate matters. To avoid bleed-over bias:
- Start a new chat for unrelated queries
- Rotate between tabs or accounts
- Clear your history when needed
You’ll get crisper, more relevant answers—and fewer confusing sidetracks.
Quick Guide: Which Model to Use When
| Model | Strengths | Watch out for… |
|---|---|---|
| ChatGPT | Structured, versatile | Can feel too safe or generic |
| Gemini | Factual, current | Sometimes shallow or disjointed |
| Claude | Ethical, nuanced, reflective | Inconsistent citations |
| Grok | Casual, concise | Less depth on complex topics |
Even free versions of these models (or open-source options like LLaMA and Mistral) work great for cross-checking. You don’t need a premium plan—just a bit of curiosity and a willingness to compare.
From AI User to Thoughtful Conductor
At first, asking the same thing to multiple models might feel like overkill. But stick with it.
Over time, this habit rewires how you think. You stop chasing “right answers” and start noticing patterns, contradictions, and assumptions—both in the AI and in yourself.
It’s not just prompting. It’s thinking in public—testing your clarity by putting it through different filters.
And when you do that, something shifts. You go from user to strategist. From passive inputter to active conductor.
Your AI Prompting Playbook
Here’s the cheat sheet version of what we’ve covered:
- Cross-Check Answers: Use 2–3 models for important questions. Compare and contrast to catch hallucinations.
- Know the Model’s Personality: Each model has strengths—and blind spots. Learn what they respond to.
- Refine Your Prompts: Try different tones, formats, and levels of detail. See what gets the best signal.
- Start Fresh Often: Avoid bias by resetting your chat, clearing memory, or switching tools.
- Reflect on the Process: If an answer is off, don’t just fix it—ask why. The question may be the real issue.
Try This Today
Think of a real question—something you actually care about. Maybe it’s creative, maybe technical, maybe ethical.
Now ask it to two or three models.
- Where do they agree?
- Where do they diverge?
- What did your phrasing assume?
You're not just collecting answers. You're training your thinking.
Final Thought: The Mirror Isn’t Flat
AI isn’t just here to give you output. It reflects your input—your clarity, your assumptions, your voice.
That reflection gets sharper when you listen to more than one echo.
When you prompt across perspectives, you don’t just avoid hallucinations—you discover how to ask better questions, with more precision, more empathy, and more range.
And that’s how you go beyond one voice.
That’s how you hear your own.