Have you ever asked an AI to summarise a document and felt that the answer was clean, but somehow thinner than the original?
That is a real pattern. It comes from compression.
Think of a summary like packing a suitcase
If you have a large pile of clothes and a small suitcase, you start making decisions.
You keep what looks most important. You fold away some nuance. You leave some items behind entirely.
An AI summary works in a similar way. It compresses a larger source into fewer words. Compression is useful, but it always risks loss.
What gets lost first
The first details to disappear are often the ones that are hardest to compress neatly:
- exceptions
- caveats
- minority viewpoints
- timelines
- numbers that need context
These details are exactly what many readers need in order to judge whether the summary is trustworthy.
Why the answer can still sound confident
Language models are good at producing smooth writing. A summary can sound well-structured even when it has flattened the original document too aggressively.
That is why polish is not the same thing as completeness.
Longer sources make this worse
When the source material is long, two problems grow:
- The model has more material to compress.
- The chance of overlooking a local detail becomes higher.
If the document is close to the model’s working memory limit, the risk increases again.
How to get better summaries
You usually get stronger results by asking for structure instead of just asking for a generic summary.
For example, ask for:
- the main claim
- supporting evidence
- open questions
- numbers worth verifying
- anything the source says with uncertainty
That prompt gives the model clearer buckets to preserve.
A good habit
Use summaries for orientation, not final judgment.
If a paragraph, clause, figure, or decision matters, go back to the source and verify it directly.
AI summaries are useful maps. They are not the landscape.