When AI produces information that sounds authoritative but is actually invented, it's not being deceptive—it's operating within its design limits, essentially pattern-matching rather than retrieving truth. Understanding why this happens helps you use AI effectively without being blindsided by invented facts.
Think of AI hallucinations like a friend who's really confident and articulate, but sometimes just makes things up without realizing it. They're not trying to deceive you—they're just generating plausible-sounding words without actually knowing if they're true.
AI systems like ChatGPT or Claude are incredibly good at predicting what word should come next in a sentence. But "next word that sounds natural" doesn't always equal "next word that's actually true." When AI doesn't know something, it doesn't say "I don't know." It invents something that sounds reasonable. This is called hallucination.
Imagine you ask AI about specific companies, salary ranges, training programs, or certifications related to your career change. If AI hallucinates details—making up a program that doesn't exist, or giving you wrong salary data—you could waste months pursuing the wrong path.
This is especially risky in career transitions where you're betting your time and maybe money on new directions.
The goal isn't to distrust AI. It's to use it for what it's good at (brainstorming, sense-making, asking better questions) while fact-checking anything specific you'll act on.
Try this: Ask an AI tool about a specific career certification or training program you're considering. Note what it tells you, then Google the same thing independently. Compare the answers and see where they differ. This builds your instinct for what to trust and what to verify.
Peri can explain this concept, give practical examples, help you decide whether it applies to your situation, or recommend a journey if appropriate.
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