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What the interviewer is testing
Whether you know that long-context use is uneven, can explain the practical impact on RAG prompts, and can name concrete mitigations and a way to test them.
Answer framework
- State the finding: models use relevant information at the start or end of a long context more reliably than information in the middle (a 2023 study showed a U-shaped curve).
- Impact: stuffing many retrieved chunks into the prompt can bury the one that matters.
- Mitigate with fewer, better chunks: retrieve and re-rank so only strong passages go in.
- Order deliberately: put the most relevant passage first, and the instructions or question near the end.
- Test on your own tasks by placing a known fact at different positions and measuring accuracy.
Sample answer
Lost in the middle describes a position effect in long prompts. In a 2023 study, models answered questions most accurately when the relevant document was at the beginning or end of the context and noticeably worse when it sat in the middle, even though it was well within the context window. Newer models handle long contexts better, but some position sensitivity often remains.
For RAG, the lesson is that more context is not free. If I paste twenty chunks and the answer is in chunk eleven, the model may miss it or blend it with less relevant text. So I retrieve wide, re-rank, and pass only the top few passages. I put the strongest passage first, keep clear source labels, and restate the question after the context.
I verify the effect on our own model and task with a simple needle-style test: insert a known fact at several positions in realistic contexts of different lengths and plot accuracy by position. That tells me how much context I can use before quality drops.
Likely follow-ups
Common mistakes
- Treating the advertised context length as usable at full quality.
- Sending as many chunks as fit instead of the best few.
- Putting the key instructions only in the middle of a long prompt.
- Not testing position effects on your own model.
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Related questions
Based on question #30 in the full question bank.