1. Understanding Attention: Your First Step to Better Prompts
If you’re human, you’re probably reading this from left to right. You might not have stopped for a moment to consider the fact that your LLM doesn’t read in the same order as you or I. Instead, it weights relationships between all tokens at once, with position and clustering dramatically changing what gets noticed.
In working with an LLM the structure you choose can have a greater impact on your results than the precise words you choose.
As a quick example both of the prompts substantively say, the same thing. However, given the way the attention algorithm works, the results of the prompts could potentially be very different.
Why the attention-optimized version works:
The key difference isn’t the words — it’s how you structure the thinking process. By breaking down the task into numbered steps as we see in option B, you’re leveraging how transformer attention works: structured, sequential instructions create clearer context that guides the model’s reasoning.
When you use an open-ended prompt like A above the model processes multiple concepts simultaneously without clear organizational structure. It has to infer the logical flow from the prompt itself, which often leads to less comprehensive or less organized responses.
The structured version works because each step provides clear context that the model can use to organize its output. “Step 1” establishes the first focus area (threat identification), and “Step 2” defines the second focus area (mitigation requirements). This step-by-step structure helps the model understand the intended logical progression and produces more organized, thorough analysis.
This is attention mechanics in action: structure influences how the model weights and relates different concepts in your prompt. Your prompt layout shapes not just what the model outputs, but how it organizes the information.
Now let’s understand how this attention mechanism actually works…
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