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Frequently Asked Questions
Can hallucination be completely eliminated?
As of 2025, hallucination cannot be completely eliminated in large language models. The fundamental cause — that LLMs are trained to generate plausible text, not to verify factual claims — means some degree of hallucination is structurally inherent. Mitigations such as RAG, grounding, and uncertainty calibration can reduce hallucination significantly for specific tasks, but no current approach achieves zero hallucination across all domains.
What types of content are most prone to hallucination?
LLMs are most prone to hallucination in: specific numeric claims (dates, statistics, prices), proper nouns (names of people, places, publications, and companies), technical details in specialised domains (medical dosages, legal citations, financial data), and events that occurred after the model's training cutoff. Hallucination rates are lowest for well-established, frequently-repeated facts that appeared many times in training data, and highest for rare, highly specific, or recent information.
How can users detect AI hallucinations?
Users can detect hallucinations by cross-referencing specific claims against authoritative sources, asking the model to cite its sources and then verifying those citations exist, testing with questions whose answers are already known, and paying particular attention to precise numbers, dates, and proper nouns. Models using RAG or tool-augmented generation reduce hallucination risk because they retrieve verifiable source documents that can be independently checked. For high-stakes decisions, human expert review of AI outputs remains the most reliable safeguard.