Mastering Context Windows: The Art of Long-Form Prompting

Modern LLMs now support context windows of 128k tokens or more. But just because you can dump an entire book into a prompt does not mean you should. Without structure, the model suffers from the "lost in the middle" phenomenon.

To get the best results, you need to treat your context window like a structured database rather than a junk drawer.

[SYSTEM INSTRUCTION] You are an expert analyst. Below is the source data provided in XML tags. Analyze the data strictly based on the provided constraints. [DATA] ... ... [/DATA]

By using XML tags, you provide the model with clear delimiters, making it significantly easier for the attention mechanism to isolate specific pieces of information.

Action Steps Summary

• Use Delimiters: Always wrap large chunks of text in XML tags or triple backticks.
• Summarize First: If the data is massive, provide a high-level summary before the raw data.
• Chain of Thought: Ask the model to outline its reasoning before it processes the long-form content.

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