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LLMs are more likely to miss information placed in the middle of a prompt
The piece describes the “lost in the middle” effect: a model may miss a requirement buried in a long prompt, such as an audit-log retention period. Engineers building context-heavy AI features need to account for this failure mode.
- What
- LLMs can be more likely to overlook information placed in the middle of a long prompt, a behavior called “lost in the middle.”
- How
- The excerpt describes testing the effect by moving the same supporting fact to different prompt positions while keeping the question fixed.
- Why it matters
- A larger context window does not guarantee correct retrieval, so systems need to account for position-sensitive use of prompt information.
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