Are AI hallucinations a data problem or a model problem?
Largely a data problem. A language model predicts plausible text; when it lacks the right grounding it fills the gap with something that sounds right, which we call a hallucination. Much of that comes from the data layer — missing context, stale or contradictory sources, poor retrieval, no single source of truth. You can't fully train hallucination out of the model, but you can starve it: ground answers in trusted, current data and the model has less reason to invent. The model generates; the data decides whether it has the truth to generate from.
AI Evaluation & ReliabilityRAG & Retrieval
Why models hallucinate at all
A language model is built to produce plausible text, not to know what’s true. Given a prompt, it predicts the most likely continuation. When the right facts aren’t in front of it, “most likely” and “correct” come apart, and it generates something fluent and wrong. Hallucination isn’t a glitch bolted onto an otherwise truthful system; it’s the default behavior of a plausibility engine without grounding.
The data layer is where the truth comes from
That reframes the fix. If the model invents when it lacks grounding, the leverage is in the grounding. Missing context, outdated or conflicting sources, weak retrieval, and the absence of a single source of truth all push the model toward making things up. Treating hallucination as a pure model defect leads to endless prompt-tweaking; treating it as a data-architecture problem points you at the actual cause.
What actually reduces it
Ground answers in trusted, current data — retrieval over authoritative sources, structured knowledge the model can lean on, clear provenance. You won’t get to zero, because the model is still a probabilistic generator. But the more reliably you feed it the truth, the less room it has to invent one. The model generates; your data decides what it has to work with.
From the conversation
This explainer is drawn from these episodes — each carries its full transcript.
