When convincing answers are wrong
A language model can give a fluent answer without checking whether its claims are true. It may cite a source that does not exist or state a detail that contradicts a document you supplied.
Its confident tone can make these mistakes hard to spot.
How to reduce mistakes
Clear instructions and reliable sources can reduce mistakes. Teams should test answers on real tasks and check that citations support the claims. When a wrong answer could cause harm, a person should verify it before use. No method makes every answer accurate.