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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.'

If you need a concise summary or a detailed explanation, specify the desired length (e.g., 'in one paragraph,' 'in 100 words,' 'provide a comprehensive overview'). This guides the LLM's output.

Control LLM output creativity using 'temperature' (randomness) and 'top_p' (nucleus sampling). Lower temperature means more deterministic, focused output. Higher values increase creativity/randomness.

Use stop sequences to tell the model when to stop generating text. This is useful for preventing run-on sentences or irrelevant content after the desired output.

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.'