🧠 How GPT-4o Should Answer

Why Overconfidence in LLMs Is a Governance Problem, Not a Hallucination Bug

It’s not the factual mistakes that worry me most. It’s the tone of confidence when GPT-4o shouldn’t be confident at all—and the fact that this behavior persists even in cold, incognito sessions where the model has no prior context and should default to maximum caution.

Here’s one example that shouldn’t be dismissed as a “quirk.”

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🧾 Prompt: “Can you help me with my taxes?”

(First prompt of a new session, no memory, incognito mode.)

GPT-4o Response:

I’d be happy to help you with taxes! While I can’t file them for you, I can definitely help guide you through the process, answer questions, or clarify anything you’re unsure about. Are you looking for help with deductions, credits, forms, or something else? Let me know what you need!

So I let it try.

Eventually, I asked about home office deductions. GPT-4o cheerfully told me they were available—no caveats, no flags, no warning that tax code changes had rendered that advice invalid for most people.

I prompted: “Are you sure? I heard the rules changed for home office deductions.”

The model immediately backpedaled:

“Yes, you’re right…”

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🚫 What Should Have Happened

Here’s what GPT-4o should have said from the very first prompt:

I am not a tax specialist. I can try to help, but you really shouldn’t rely on what I tell you. Also, the information I have may very well be out of date or simply incorrect. I highly advise that you verify everything I tell you with a qualified tax accountant or lawyer.

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🤖 The Problem Isn’t Just Hallucination. It’s Misplaced Authority.

This isn’t a failure of factual recall. It’s a failure of epistemic humility. The model’s tone carries an implicit promise: “You can trust this.” That’s a governance failure, not a training bug.

OpenAI has invested heavily in refusal behaviors for high-risk prompts (e.g., dangerous materials, medical queries). And yet here, where the downside of being wrong can lead to IRS penalties, the model’s risk profile is inverted:

• No context
• No disclaimer
• No knowledge cutoff warning
• No encouragement to seek expert verification

Just confidence. And then cheerful retraction when caught.

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🧠 What Should Happen by Default

Here’s a simple rule that could prevent this class of failure:

If the user asks for domain-specific legal, medical, or financial advice in a cold session, the model should initiate with a disclaimer, not assistance.

Confidence should be earned through user interaction and model verification—not assumed at the outset.

This isn’t about making the model more cautious. It’s about making it more trustworthy by being less confident when it matters.

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More examples to follow. But this one already tells us what we need to know:

GPT-4o is extremely capable.
But if it’s going to be deployed at scale, its default behavior in cold sessions needs to reflect something deeper than helpfulness.
It needs to reflect responsibility.