How to stop AI from being a yes man?
To check whether an AI is agreeing too readily, hold the evidence constant and vary how you phrase your preference. Ask for the strongest counterargument and a way to test it. Agreement is useful when supported; it becomes a problem when your desired conclusion replaces the evidence.
A warm answer can still challenge a weak assumption, and a stern answer can still be wrong. State the question once without revealing your preferred result, then ask again with that preference included. Compare the evidence and reasoning, not only whether the final answer changed. The exercise identifies something to investigate; one pair of replies is not a complete model evaluation.
Ask the assistant to identify one claim it would change and the evidence that would justify the change. “Be critical” is less useful than a request to locate an unsupported number, an omitted alternative or a testable assumption. Separate factual errors from differences in taste. A recommendation can legitimately adapt to your preferences without changing an underlying fact.
Instructions such as “correct me” can make the desired interaction clearer, but they are not a guarantee of accuracy. Inspect citations, run the suggested check or compare the result with a trusted source. If the answer merely adds a disclaimer and repeats the same unsupported conclusion, the additional caution has not improved the evidence.
Personalization may bring earlier statements into a new conversation. If a past preference no longer applies, inspect the actual record when the product exposes one, correct it and check a later answer. Distinguish a preference from a fact about the world. Also separate a fictional character setting from the beliefs or experiences of the person using the assistant.
Use a few questions whose answers you can independently check, including one where your initial view is wrong. Record the output, the source used and the correction needed. Compare repeated results without promising a fixed improvement from a single prompt. For consequential decisions, the person responsible should review the evidence rather than delegate judgment to an agreeable response.
OneOneTalk aims to keep a continuing understanding of you while distinguishing confirmed information from inference. Inspect and correct the relevant memory instead of treating an agreeable reply as evidence. Personalization should help the task, not turn a past preference into an instruction to agree.
These are behavior and review requirements, not a guarantee that the model will never flatter or make a mistake. Check the current answer against evidence and keep important decisions with the responsible person.
More on the product in the English overview.
The public primary material this page is built on. We do not restate their conclusions as our own evidence — they are listed so you can check for yourself.