Description
LLMs have limited attention. Everything you load into context competes for that attention.
When too much is loaded at once, the model either:
- Dilutes attention across everything (stays shallow)
- Fixates on the wrong parts (misses what matters)
Impact
- Worse performance on all tasks when context is too broad
- Even explicit ground rules get ignored
- A longer, focused context outperforms a shorter, scattered one
