Prompt Templates

here is the complete structure for advanced prompt templates:

Start with a problem statement or research question that provides context and focus for the prompt.
Identify the specific task or objective that the prompt is designed to achieve.
Specify multiple input variables that provide additional context and specificity to the prompt, such as age group, income, education level, geographic location, or health condition.
Provide a detailed analysis of the topic or task, including an exploration of relevant theories, best practices, case studies, and data-driven insights.
Offer guidance on how to approach and complete the task, including specific steps, examples, and feedback loops.
Encourage creativity, innovation, and critical thinking by promoting unconventional or experimental approaches to solving the problem or achieving the objective.
Consider using natural language processing techniques such as named entity recognition or sentiment analysis in the prompt, to enable more nuanced and context-aware responses, but without requiring ownership of the language model.
Provide guidance on how to evaluate the quality of the outputs, by defining clear criteria for accuracy, relevance, coherence, and diversity, without the need for advanced ensembling or machine learning techniques.
Consider incorporating user engagement and interaction elements into the prompt, such as personalization, gamification, or social dynamics, to increase user motivation and participation, but without the need for complex algorithms or models.
Provide transparency and explainability by giving users a clear understanding of how the prompt is structured, what data and algorithms are being used, and what limitations or biases may exist, without requiring access to the underlying language model.
By following this structure, advanced prompt templates can be optimized for quality, effectiveness, and accessibility, while also promoting creativity, innovation, and critical thinking. Users can achieve even the most complex and sophisticated tasks, while also gaining a deeper understanding of the underlying theories, best practices, and data-driven insights, without the need for advanced machine learning or natural language processing techniques.