Observation: Correct but incomplete model answers
In daily interactions with language models, I keep seeing a pattern.
Many answers are factually correct and linguistically clear, yet they still feel incomplete.
Not wrong, but missing part of the full context.
This seems less about missing knowledge and more about how context is selected under uncertainty.
Working hypothesis
Missing context appears in different forms:
• Intent gap
When the user’s goal is not explicit, the model defaults to general and safe explanations.
• Actor gap
The answer explains the topic but not clearly for whom it matters.
• Importance distribution gap
Relevant details exist but the most important ones are not emphasized.
• Temporal or situational gap
The answer is correct in theory but detached from real use or timing.
The model does not choose the wrong context. It narrows the context too early.
Why this matters for evaluation
These answers
are correct by accuracy metrics
do not mislead directly
but still shape how the user understands the topic
So the real question becomes
Which context was highlighted and which was ignored
Open questions
Have others noticed this
Has anyone tried to measure context coverage or importance
How do you evaluate answers that are correct but incomplete
Looking ahead
I want to collect these cases and analyze which signals cause early context narrowing.
This feels like an important evaluation space that accuracy alone does not capture.