Mastering Context Windows: The Art of Long-Form Prompting

As LLM context windows expand to 128k, 200k, and even 1M tokens, the challenge has shifted. It is no longer about fitting data into the window; it is about ensuring the model actually pays attention to the right parts of that data.

When you dump a 50-page PDF into a prompt, you risk the "Lost in the Middle" phenomenon, where models prioritize information at the very beginning and very end of the prompt, ignoring the substance in the middle.

SYSTEM: You are an expert analyst. You will be provided with a long-form document. 1. Summarize the core thesis in 3 sentences. 2. Extract the 5 most critical data points. 3. Identify any contradictions between the introduction and the conclusion. DOCUMENT: [INSERT FULL TEXT HERE]

To combat this, use a "sandwich" structure. Place your most important instructions at the very top, provide the data, and then restate your specific requirements at the very bottom before the model begins its response.

Action Steps Summary

• Pre-process: Strip unnecessary whitespace and boilerplate from your source text.
• Sandwiching: Repeat your core task instructions at the end of the prompt.
• Chain of Thought: Ask the model to "outline its reasoning" before providing the final answer to force it to scan the full context.

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