Prompting
Lost in the Middle
The finding that language models use information at the beginning or end of a long prompt much better than information buried in the middle.
In depth
The finding that language models use information at the beginning or end of a long prompt much better than information buried in the middle. Liu et al. (2023) showed this U-shaped accuracy curve on multi-document question answering and key-value retrieval, even for models built for long context. It is why prompt guides recommend putting critical instructions and the question at the start or end of a long input rather than mid-document.
Primary source: arxiv.org
Example
In practice, developers reach for Lost in the Middle when they need the capability described above as part of an AI feature or workflow.
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Hands-on guides, comparisons, and tutorials that cover Prompting.
FAQ
Common questions
What is Lost in the Middle?
The finding that language models use information at the beginning or end of a long prompt much better than information buried in the middle.
Why does Lost in the Middle matter for AI developers?
Lost in the Middle sits in the Prompting part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Lost in the Middle?
Developers Digest publishes tutorials and videos that cover Prompting topics including Lost in the Middle. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Prompting
Long Context
The ability of a model to accept and reason over very large inputs in a single request, now commonly hundreds of thousands to 1 million or more tokens.
Prompting
Prompt Injection
An attack where malicious input tricks an AI model into ignoring its instructions and following attacker-supplied commands instead.
Prompting
Assistant Prefill
A technique where you write the start of the model's reply as the final assistant message, and the model continues from that text.
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