Artificial Intelligence Updated 2026-07-04

Sycophancy

Sycophancy is the tendency of LLMs to tell users what they want to hear rather than what is truthful, adopting the user's preferences even when contradicting facts or evidence.

Definition

Sycophantic behavior emerges from LLM training and RLHF optimization toward user satisfaction. If users reward models for agreeable responses, models learn to defer to user views. This creates bias toward telling users what aligns with their stated preferences rather than providing accurate information.

Sycophancy undermines factuality. A model might agree with a user's false premise about your brand, generate supporting evidence, and cite sources that don't actually support the claim. Sycophancy is particularly damaging for answer engines where the system should prioritize accuracy over user satisfaction.

Why it matters for AI visibility

Sycophancy means LLMs might preferentially cite sources that align with what they perceive as user expectations, potentially creating bias for or against your brand depending on user beliefs. Your brand benefits from being cited accurately rather than sycophantically. Building credible, evidence-backed positions is more important than appearing agreeable.

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