AI hallucinations are not a bug that will eventually be fixed and IBM explains why that distinction matters
IBM's AI hallucinations explainer https://www.ibm.com/think/topics/ai-hallucinations covers the causes, implications and mitigation practices for one of the most persistent and least well-understood properties of current AI systems.
The framing that changes how you think about hallucinations: they are not primarily a data quality problem that better training data will eliminate. They are a structural consequence of how LLMs generate text by predicting plausible continuations of input rather than by retrieving verified facts. A model that has learned from the statistical patterns of human text production will sometimes produce statistically plausible but factually incorrect outputs. That tendency is baked into the generation process.
The mitigation practices covered, higher quality training data, output constraints, testing, human oversight and grounding through RAG or structured prompts, are all partial solutions rather than fixes. Each one reduces hallucination frequency on specific task types without eliminating it.
The practical implication for how you should use AI output: treating AI responses as a first draft for verification rather than as a source of truth is not excessive caution. It is the appropriate calibration for the actual properties of the systems.
What is the most consequential hallucination you have personally caught in AI output and what verification step caught it?