embedding check

A quick gut-check moment — usually mid-conversation or mid-project — where you pause to ask whether you're working from actual facts or just filling in gaps with assumptions. An inference check is the intellectual equivalent of receipts: are you reading what's actually there, or are you pattern-matching your way to a conclusion that feels right? In AI contexts it refers specifically to auditing model outputs to verify they reflect real data rather than confident-sounding guesses. In everyday use it's calling yourself (or someone else) out for jumping to conclusions.

An LLM check is the act of running something past an AI language model to verify, fact-check, summarize, or improve it before trusting or using it — the 2020s equivalent of Googling something, but more conversational and more prone to confident-sounding errors. When someone says they're doing an LLM check, they're acknowledging both the utility and the skepticism: the model might be right, or it might be hallucinating with complete conviction. LLM checks have become a reflex for students, writers, and professionals navigating a world where AI assistance is ubiquitous.

A moment of honest self-audit where someone examines whether their manifestation practice is actually aligned with their intentions — checking if they've been consistent, specific, and emotionally committed enough to make the universe supposedly deliver. Manifest check is also used socially as a gentle or ironic prompt to ask a friend whether they've been keeping up with their manifestation rituals. It lives in the overlap of Gen Z wellness culture and self-aware irony, equal parts earnest accountability tool and good-natured mockery of the whole enterprise.