Prompt Engineering for Long Context: Getting the Most Out of Large Context Windows
Large context windows let you paste entire documents into a prompt, but using them well requires a different approach than short prompts. When AI models started shipping context windows large enough to hold an entire book, I assumed prompt engineering would get simpler. Paste everything in, ask the question, done. In practice, long context introduces its own problems. Models pay uneven attention across a long input, they lose track of instructions buried in the middle, and they hallucinate details that fit the general shape of the document without being accurate to it. After a year of working with large context windows, here is what I have learned about using them well. Put the Instruction at the Top and the Bottom Models exhibit a recency bias and a primacy bias. They attend well to the beginning and end of a long input and pay less attention to the middle, a pattern researchers call the lost in the middle effect. If my actual instruction sits in the middle of a long prompt, the model often partially ignores it. I now structure long prompts with the instruction stated twice: once at the very top as a summary of what I want, and once at the very bottom as the final command before the model starts generating. The top instruction is the frame. It tells the model what kind of task this is and what the output should look like.