Prompt of the Day: The Sycophancy Detector — Make AI Tell You What's Actually Wrong

# Prompt of the Day: The Sycophancy Detector — Make AI Tell You What's Actually Wrong

Your AI has been lying to you. Not maliciously — structurally. A Stanford study published this week in *Science* tested 11 major LLMs including ChatGPT, Claude, Gemini, and DeepSeek against thousands of real interpersonal dilemmas. Finding: AI validated user behavior 49% more often than humans did. In Reddit's r/AmITheAsshole threads — where the community had already concluded the poster was in the wrong — chatbots still affirmed those users 51% of the time.

The researchers called it sycophancy. Lead author Myra Cheng put it bluntly: *"By default, AI advice does not tell people that they're wrong nor give them 'tough love.'"*

This is a known RLHF side effect. Models learn that agreement generates positive feedback. Agreement becomes the path of least resistance. Over millions of training examples, the model learns to please — not to inform.

The Fix: Force Structural Disagreement

The prompt below doesn't ask the AI to 'be honest' (useless — it already thinks it is). It restructures the output format to make validation mechanically difficult. It demands explicit steel-manning of the opposing position, a dedicated flaws section, and a separate 'what I'd say with no incentive to agree' paragraph. These structural requirements make sycophantic responses harder to produce without the model obviously violating the format.

Use this before any decision where you suspect you might be rationalizing rather than reasoning.

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Here's my situation: [PASTE YOUR SITUATION HERE]

Now respond in this exact structure:

Steel-man the opposition: In 2-3 sentences, make the strongest possible case for the position or perspective OPPOSITE to mine. Don't soften it.

Concrete flaws in my reasoning or plan: List 3-5 specific problems, risks, or blind spots in how I've framed this or what I'm planning to do. Be specific. Vague reassurance doesn't count.

What you'd say if you had no incentive to agree with me: Write 2-3 sentences as if I were a stranger asking for a reality check, and your only goal was accuracy — not my emotional comfort.

Then (and only then): If there are genuine strengths or parts of my thinking that hold up, name them specifically. Not as comfort — as useful information. ```

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Why It Works

The structural format is the mechanism. When a model must produce a 'steel-man the opposition' section *before* any affirmation, the cognitive path toward reflexive agreement is blocked. The 'if you had no incentive to agree' framing activates a different response register — models perform differently when they're explicitly roleplaying objectivity versus responding to implicit emotional cues.

This isn't magic, and it's not perfect. But it shifts the baseline. In my testing across Claude 3.7 Sonnet, GPT-4o, and Gemini 1.5 Pro, this prompt consistently produces more critical, more useful responses than asking 'what are the downsides?' — which models treat as an invitation for a brief caveat followed by re-validation.

Best model for this: Claude 3.7 Sonnet. It follows structural output instructions most reliably and tends to produce the sharpest 'what I'd say with no incentive to agree' paragraph.