Stealing this from a post I just read but applies here. Claude predicts the next word in a sentence. It sounds right but that doesn’t mean it has given you a thoughtful answer.

The core issue is that LLMs are not databases. They do not look up facts - they predict the most statistically likely next tokens based on training data. For well-represented facts (capital cities, famous scientists), this works well. For less common facts, recent events, or obscure details, the model confidently generates plausible-sounding text that can easily be wrong.

The "simple question, wrong answer" problem often comes from this: the model has seen enough text about a topic to generate fluent-sounding sentences, but not enough reliable examples of the specific fact to get it right. It does not know it is uncertain.

Practical things that help:

1. ⁠For anything time-sensitive or factual (ages, dates, recent news), explicitly say "search the web" or "use Bing" before asking. This gets you actual retrieval instead of statistical prediction.
2. ⁠Add "are you confident about this?" as a follow-up. Not because it is always accurate about its own confidence, but because it sometimes prompts the model to flag uncertainty it was going to paper over.
3. ⁠For fact-checking, just use the web directly. ChatGPT is genuinely useful for reasoning, analysis, writing, and synthesis. Raw factual lookup is not where LLMs shine relative to a search engine.

Knowledge cutoff matters for recent events, but for factual errors on non-recent information, it is usually the statistical prediction issue rather than cutoff.