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For complex tasks, don't try to write the perfect prompt in one go. Start with a simple instruction, see the result, and then iteratively add more constraints, context, and examples to refine the output until it meets your requirements. This is more effective than starting with an overly complex prompt.

To guide the model away from undesired outputs, tell it what *not* to do. For example, add constraints like 'Do not use technical jargon,' 'Avoid mentioning price,' or 'The response should not be longer than two paragraphs.'

Provide a few examples (input/output pairs) in your prompt to guide the LLM's response. This is called few-shot prompting and can significantly improve performance on specific tasks.

Start the desired output for the LLM. For instance, if you want a list, start your prompt with the beginning of the list: 'Here are the steps:
1.' This can guide the model effectively.

Adding constraints to your prompt (e.g., word count, specific keywords to include/exclude, format requirements) can often lead to more focused and useful outputs from the LLM.

For intricate tasks, consider a structured prompt with sections like: ROLE, CONTEXT, TASK, OUTPUT_FORMAT, EXAMPLES. This organization helps the LLM understand requirements better.

Review your prompts for ambiguous words or phrases that could be interpreted in multiple ways. Strive for explicitness.

For complex tasks, reveal information progressively. Start with basic requirements, get initial output, then add more specific constraints or details.

Establish a protocol: 'After each response, I'll provide feedback. Use this feedback to improve your next response while maintaining the core requirements.'