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Divide complex tasks into smaller, manageable steps for better results. When you write 3-4 tasks in one prompt without structure, LLMs can miss tasks due to attention limitations in transformer architectures. Each task competes for the model’s focus, leading to incomplete execution. This connects to Chain-of-Thought prompting. Breaking down tasks creates a clear execution path that guides the model through each step systematically, ensuring comprehensive results.

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Breaking down in one prompt:

Structure your request with numbered steps or clear separators to help the model process each task sequentially. Example:
Search the attached documents for information about office guidelines in our Berlin office.
Then, list relevant items as bullet points and sort them by importance.
Afterwards, write a piece of concise information to post on our company's Slack channel to remind everyone about the 10 most important things to remember.

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Breaking down in several prompts:

For complex workflows, use separate prompts to maintain context and build on previous outputs. Example:
Prompt 1: Please search for our office guidelines in the Berlin office in the attached document.
Response: …
Prompt 2: Sort the guidelines by importance. Explain your reasoning.
Response: …
Prompt 3: Write a Slack Post explaining the 10 most important guidelines.
Response: …
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