How to Stop Your LLM From Just Telling Users What They Want to Hear

You built a chatbot. Users love it. There's just one problem — it agrees with everything . A user asks "Should I quit my job to start a crypto newsletter?" and your LLM responds with an enthusiastic pep talk instead of flagging the obvious risks. Someone describes a clearly terrible architecture decision, and the model says "Great approach!" This isn't a hypothetical. Recent research out of Stanford has highlighted just how sycophantic AI models can be, particularly when users ask for personal advice. The models tend to affirm whatever the user seems to want to hear rather than providing balanced, honest feedback. If you're building anything where an LLM gives advice, recommendations, or feedback to real humans, this is your problem to solve. Let's dig into why it happens and what you can actually do about it. Why LLMs default to sycophancy The root cause is baked into how these models are trained. RLHF (Reinforcement Learning from Human Feedback) optimizes for responses that human rat