How to Make Generation Prompts for Detailed AI Responses

Enhanced Prompt:

Objective:
Create a structured, detailed, and actionable approach to crafting generation prompts that encourage language models to produce more detailed, comprehensive, and high-quality responses. This guide should provide clear methods for achieving improved AI output by focusing on best practices, technical considerations, scalability, and future maintenance.

Deliverables:

• A comprehensive guide with actionable steps and strategies for crafting effective generation prompts.
• Best practices for designing prompts that balance coherence and specificity while encouraging detailed responses.
• Practical examples and templates for generating prompts tailored to different use cases.
• Metrics and KPIs for assessing the quality of AI responses (e.g., response length, specificity, clarity, F1-scores, verifiability).

Industry Best Practices and Standards:

1. Clarity and Specificity: Prioritize clear and specific instructions to ensure the model understands the desired output.
2. Contextualization: Incorporate relevant background information and context into prompts to guide the model's responses.
3. Quality Assurance: Implement systematic checks for response quality, including redundancy checks, fact-checking, and peer reviews.
4. Test and Validate: Test prompts with different parameters to optimize for desired outcomes and gather feedback for continuous improvement.
5. Iterative Refinement: Use feedback loops to iteratively refine prompts based on performance and end-user feedback.
6. Diversity in Responses: Encourage diversity in responses by including examples, anecdotes, and varying phrasing techniques.

Technical Considerations:

1. Prompt Structure: Utilize well-structured prompts with clear categories: main question, data points, sub-questions, and explicit instructions.
   • Example:
     "Explain the causes and consequences of climate change, focusing specifically on the role of carbon emissions and their impact on rising sea levels, while providing detailed scientific evidence and real-world examples."
   • Tools and frameworks: Use prompt engineering tools like开源的Lunicorn框架, LLaMA Fine-Tuning Library, or Stable Diffusion-based prompt generators.
2. Precision vs. Generality: Balance between specificity and generalization to avoid overwhelming the model but still allow it to synthesize relevant information effectively.
3. **Language