Effective prompts are essential for guiding language models and should prioritize simplicity and clarity. Prompts must clearly outline specific tasks while avoiding unnecessary complexity, encouraging developers to integrate prompts thoughtfully with tools and systems tailored to their use cases[1]. Providing explicit actions and breaking down complex instructions into smaller, manageable steps can significantly enhance the model's ability to follow them, leading to more reliable outputs[3].
Additionally, using clear instructions and illustrative examples improves accuracy by sharpening the model's focus on relevant content. It’s beneficial to utilize simple language, set a maximum token length, and experiment with different input formats to determine the most effective approach for specific tasks[2].
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