AI Ep 32: The Myth of the All-Knowing Model

Let’s reset the narrative for a second. Large language models don’t actually know things. They aren’t pulling from a live database. They don’t check facts in real time. What they do exceptionally well is predict language.

So when you hear someone say, “ChatGPT sounded confident, so it must be right,” that should raise a red flag.

These tools generate answers based on patterns, not certainty. There’s no internal meter saying, “I’m pretty sure this is accurate.” They’re optimized to sound authoritative whether the information is solid or completely off base.

That’s where hallucinations come in. Fabricated quotes. Data assigned to the wrong sources. Details that feel believable but were never true to begin with.

If you’re using AI as a source of truth, you’re setting yourself up for trouble.

The better move is to treat it like a thinking partner. Use it to spark ideas, outline a draft, or pressure test your thinking. Then do the real work: verify, validate, and apply judgment. AI should help you get started, not sign off on the final answer.

Bottom line: confidence is not accuracy. Use AI with intention, and always verify what matters.

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