What is the lost-in-the-middle problem in long-context RAG?

Lost-in-the-middle is the observation that language models pay less attention to information in the middle of long contexts than at the beginning or end, causing them to miss or ignore relevant passages. It was documented by Liu and colleagues in 2023 and applies to both leading proprietary and open-source models. For RAG, this means simply stuffing 50 chunks into a long context performs worse than carefully selecting and ordering 5 to 10 chunks. Put the most important context at the start or end, not buried in the middle.