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Tell the model what NOT to do in addition to what you want. For example, 'Explain quantum computing without using analogies or metaphors.' Negative constraints often produce more focused and accurate outputs than positive instructions alone.

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.'

Sometimes it's effective to tell the LLM what to avoid. For example, 'Write a product description. Do not use clichés or overly technical jargon.'

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.

Clearly list all constraints the output must satisfy. For example, 'Write a poem about a cat that is exactly 10 lines long and mentions the moon.'

Structure prompts by defining: WHAT you want the LLM to do, WHY it's important (context), and HOW it should do it (format, style, constraints).

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

If initial constraints are too restrictive and yield poor results, gradually relax them: 'If the previous constraints are too limiting, suggest the closest possible alternative.'