Where in a long prompt should you place the most critical context: at the start, the middle, or the end?

LLMs do not attend evenly across a long prompt. They attend more to content at the start and at the end of the context, and less to content in the middle. The pattern looks like a U when you graph retrieval accuracy against position. The practical rule that falls out of this is simple. Put your most important context at both ends. The system prompt and role definition go at the top. Key constraints, the user query, and a final restatement of any critical rule go near the bottom, right before generation starts. The middle is where retrieved chunks, examples, or supporting material that you can afford to lose can sit. The effect is well-documented. It persists across model sizes and across newer long-context models. Even when a model technically supports 128k or 1M tokens, the U-shape does not go away, it just gets stretched.

The empirical finding is that LLMs exhibit U-shape position bias on long contexts. They retrieve information from the start and end of the prompt reliably, and they degrade in the middle. The original paper from 2023 documented this for retrieval QA over long passages, and it has been reproduced across model families and across long-context architectures. The practical rule is to use both ends of the prompt as high-attention real estate. Top of the prompt: system prompt, role definition, output format, the most important global constraints. Bottom of the prompt: a restatement of the key constraint, the user query itself, and any inline reminder of format. The middle is where retrieved documents, long examples, or material you do not need verbatim recall on can live. In RAG specifically, this changes ordering. Rather than dumping retrieved chunks top to bottom by score, you place the most relevant chunk last (just before the query) and the next most relevant first. Lower-relevance chunks fill the middle. The result is measurable on grounded-QA evals: same retrieval, better answers.