Outputs feel generic

Generic outputs usually mean the model understands the task at a surface level but not the specific context that makes the response feel useful. The answer may be correct, but it lacks the detail, tone, or angle that makes it stand out.

This often happens when prompts are too broad or examples are too average. The model then defaults to safe language that sounds polished but not distinctive.

Better context, stronger constraints, and more concrete examples can help a lot. Specificity is usually what turns a generic answer into one that feels real and relevant.