AI "Gets Things Wrong Plausibly"
ChatGPT, Claude, and Gemini all sometimes answer things that aren't true, in confident, natural prose. This is called "hallucination," and by the nature of how AI works it can't be reduced to zero. Because the output reads well, beginners especially tend to take it at face value. Before using it for work, it matters to separate what it's good at from what it's weak at.
Why It Happens (Roughly)
Large language models build text by picking "the likely next word" by probability. Information not in the training data, or probabilistically "plausible" but wrong information, comes out as natural sentences too—because "answering plausibly" looks better in training than saying "I don't know."
Common Hallucination Patterns
- Inventing non-existent sources: confidently presenting fake paper/book titles, URLs, statute numbers
- Fabricating numbers/dates: making up baseless stats like "the market is X trillion"
- Over-filling vague questions: asserting via guesses when premises are missing
- Over-completing in long summaries: adding conclusions not in the original
- Mixing misinformation to fill out comparison-table cells
Especially dangerous is filling in without saying "I don't know." Because output is natural, errors are hard to notice.
