- Clear task descriptions: Provide detailed and unambiguous instructions for the desired output[5].
- Consistent formatting: Maintain a consistent structure and language across prompts[5].
- Iterative refinement: Use feedback and results to continuously improve prompts[6].
- Model selection: Choose LLMs with larger vocabularies and diverse pre-training corpora[7].
- Prompt stability analysis: Assess the consistency of LLM outputs across multiple runs with the same prompt[5].
- Ethical considerations: Be mindful of potential biases and ethical implications when designing prompts[5].
By incorporating these frameworks and best practices, researchers and practitioners can improve the quality and reliability of LLM-generated responses across a wide range of tasks.