Accuracy drops sharply on domain specific queries

Many models look strong on broad benchmark tasks and then fall apart when users ask domain-specific questions. That usually happens because the model has enough general language skill but lacks the exact terminology, constraints, or edge-case knowledge required in the field.

Domain queries also expose weak retrieval and weak evaluation. A model may seem accurate on common examples while failing badly on the rare, technical, or business-critical ones that matter most in production.

The fix is usually not more generic training. It is better domain data, better retrieval, and a benchmark built from real queries instead of abstract test cases.