What is 'lost in the middle' and how do you mitigate it?

30-second answer

Models tend to use information at the beginning and end of a long context better than information in the middle. Mitigate by sending fewer, better-reranked chunks, placing the most relevant ones at the edges, and keeping prompts focused rather than stuffing the context window.

Try answering out loud before reading on. The walkthrough below is paced for a 40-minute interview round.

0–5 min Clarify and frame

Framing: "Models don't use every position in a long context equally. I'll describe the effect, why it likely happens, how to test for it, and design mitigations."

5–15 min Core explanation

Research on multi-document QA showed a U-shaped curve: accuracy is highest when the relevant document is at the start or end of the context and lowest when it's in the middle, especially as the number of documents grows. Newer models trained for long context have reduced but not eliminated position sensitivity, and distractor documents still hurt.

Likely contributors:

Training data where important information sits at beginnings (titles, intros) and ends (the latest turn).
Attention patterns and positional encodings that favour recent tokens and early "anchor" tokens.
More context means more plausible distractors competing for attention.

15–25 min Deep dive: mitigations

Mitigation	Why it helps
Rerank and send fewer chunks (e.g. 3–6, not 20)	Less distraction, less middle
Relevance thresholds	Drop weak matches entirely
Order by relevance at both edges	Best chunk first, second-best last
Put the question after the documents	Recency helps the model focus on what's asked
Ask for quotes before answering	Forces the model to locate evidence
Map-reduce over chunks	Extract per chunk, then combine
Compress context	Summarize or extract relevant sentences first

25–32 min Testing for it

Needle-in-a-haystack and multi-needle tests at different depths and total lengths.
In your own eval set, vary the position of the gold passage among distractors and plot accuracy by position.
Test with realistic distractors (similar documents), not random filler, which overstates performance.

32–37 min Production example

A policy assistant passing 15 chunks scored well when the answer was in chunk 1 but poorly at chunk 7–9. After adding a reranker, a top-5 cutoff and ordering the best chunk first and second-best last, accuracy became much flatter across positions and prompts were half as long.

37–40 min Likely follow-ups

Do long-context models fix this? They help, but effective use of very long contexts still varies; test with your data.
Does prompt caching change the strategy? Caching makes static long prefixes cheaper, but doesn't fix attention dilution.

What interviewers listen for

Knowing the U-shaped effect and that distractors matter.
Practical ordering and filtering mitigations.
A way to measure it.

Red flags

"Just use the biggest context window."