Training
AI Hallucinations: Why Models Make Things Up
Why confident-sounding wrong answers are a natural consequence of how these models are trained, not a rare bug.
It's not a glitch — it's the objective
An LLM is trained to produce text that's statistically plausible given everything it's seen, not text that's verified true against the world. During pretraining, there's no built-in mechanism that flags "this sentence is factually correct" versus "this sentence merely sounds correct" — both can be equally plausible completions of a prompt. A hallucination is what happens when a model generates a fluent, confident-sounding statement that simply isn't true, whether that's a fabricated citation, a wrong date, or an invented API method that doesn't exist.
Why RLHF can make this worse, not better
The training stage meant to make models more helpful can actually amplify the problem. RLHF rewards responses that human raters preferred, and raters tend to prefer answers that sound confident and complete over ones that hedge or admit uncertainty — even when the hedging answer is more honest. Over enough training, this can teach a model that a confident wrong answer scores better than an uncertain right one, unless the training process specifically corrects for it.
Why longer context doesn't fully fix it
More context window or retrieval of real documents reduces hallucination risk for tasks grounded in that provided information, but it doesn't eliminate the underlying issue — a model can still misremember or misstate something even with correct information sitting directly in its context, and it will still generate a plausible-sounding answer when asked something entirely outside the provided material, rather than reliably recognizing "I don't actually know this."
What actually helps
Providers reduce hallucination rates over successive model generations through better training data curation, RLHF specifically tuned to reward calibrated uncertainty, and techniques like having models cite sources they can be checked against. As a user, the most reliable mitigation is still an old one: verify anything that matters before you rely on it, especially specific facts, citations, and numbers.