A URL to respond with when your boss says "But ChatGPT Said " (stopcitingai.com)

Large language models like ChatGPT, Claude, and Gemini don’t “know” facts the way a database or expert does — they compute which words are most likely to follow a given prompt. That probabilistic token-prediction process can produce fluent, confident-sounding answers that nevertheless lack provenance or accuracy. Because models are trained on massive text corpora without explicit source-tracking and optimized for next-token likelihood, they can hallucinate concrete claims, echo biases in their data, or become sycophantic (telling users what they seem to want to hear). Real-world consequences cited in recent studies and reporting include damage to scientific integrity, legal errors in filings, and dangerous medical advice when users overtrust outputs. For practitioners and the broader AI/ML community, this matters both technically and operationally. Technically, hallucinations arise from model objectives, exposure bias, and missing grounding — so fixes include retrieval-augmented generation, stronger provenance/citation systems, calibrated uncertainty estimates, and human-in-the-loop validation. Operationally, outputs should not be copy-pasted as authoritative: verify with primary sources, instrument models to show sources and confidence, and deploy guardrails where errors are high-stakes. The takeaway: LLMs are powerful language predictors and productivity tools, but safe and responsible use requires treating their answers as hypotheses to be checked, not as established truth.