Outputs are technically right but not helpful in real scenarios

This is one of the most revealing failure modes in AI. The answer can be factually accurate and still leave the user unsatisfied because it does not solve the actual problem sitting behind the question. Correctness matters, but usefulness is what users remember.

Sometimes the model gives a technically precise answer when the user really needed judgment, prioritization, simplification, or a next step. In those moments the system sounds smart while being practically unhelpful.

The way forward is to evaluate answers in context, not isolation. Ask whether the response helped the user decide, act, or move forward. That shift changes how prompts are written, how outputs are scored, and how product teams define success.