Being polite. Choosing the newest model. Thinking all AIs are the same. These are just a few of the common myths that stand in the way of successful AI adoption. Let's debunk them - one by one.**
AI is chaning fast, but the way we interact with it often lags behind. Whether you're experimenting with GPTs or building complex automation, old habits and misunderstandings can lead to underwhelming results. Here are five myths about AI that are surprisingly common, and what to do instead.
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1. “You need to be polite to get better results”
“Could you please...”, “Would you mind...?”, “Thanks.”
We see it all the time in ChatGPT prompts – and while it’s nice to be nice, it doesn’t help your output. In fact, it can confuse the model.
AI isn’t human. It doesn’t reward politeness. Vague, polite phrases add noise and make your intent harder to interpret. Instead, be clear, structured and specific in what you want. It’s the most respectful – and effective – way to interact with AI.
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Do this instead: Use bullet points, define roles, include examples, and state clearly what you want.
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2. “Newer models are always better”
Not necessarily. While newer models often improve on reasoning and language, they can also be slower, more expensive, or less predictable in formatting. In some cases, a smaller, earlier model might be faster and more efficient for your specific use case.
Tip: Test before you switch. Your best fit might not be the latest release.
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3. “All AI models work the same way”
They don’t. GPT-4, Claude, Gemini, Mistral – each has its own personality, strengths and quirks. Some are better at creative writing, others at code or summarisation. Knowing the difference can save you hours and help you choose the right tool for the job.
Example: Gemini might outperform ChatGPT in summarising long texts, while GPT could give sharper marketing copy. Clause might be the best provider for code.
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4. “AI understands what you mean”
It doesn't. AI predicts likely continuations based on text patterns – it doesn’t actually understand context or intent like a human does. If you’re vague or leave gaps, the model will guess – and not always correctly.
That’s why precision matters more than ever. State exactly what you want, and never assume the AI will “read between the lines”.
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5. “You need to learn prompting to use AI effectively”
It depends. When working directly in tools like ChatGPT, structured prompting is critical. But when you work with a well-built AI agent, the need to prompt disappears – because the prompting has already been done for you.
AI agents are designed to handle specific tasks based on pre-set instructions, system messages, and defined workflows. Instead of writing prompts, you just provide input – and the agent knows what to do. This makes AI far more accessible across a team, without requiring anyone to learn the art of prompt engineering.
The result? Less trial and error, faster output, and more consistent results.