What is the lost in the middle problem?

Lost in the middle is the observation that LLMs often pay less attention to information placed in the middle of a long context compared to the beginning or end. This means that even models with large context windows do not use them uniformly. For RAG applications, this affects how you order retrieved chunks, often placing the most important content at the start or end.