Tips for Writing Prompts on Economic Systems | Open-Ended Questions

Enhanced Prompt:

Title: Crafting Prompts to Analyze the Impact of Economic Systems and Models

Objective:
To design and implement high-quality, open-ended prompts that effectively encourage language models to analyze and evaluate the impact of various economic systems and models. The prompts should be structured to elicit detailed, insightful, and actionable insights while adhering to industry best practices and standards.

Deliverables:

1. A comprehensive set of 10+ open-ended prompts tailored to explore the impact of different economic systems (e.g., capitalism, socialism, mixed economies) and models (e.g., Keynesian, neoclassical, supply-side economics).
2. A detailed analysis of the prompts, including their effectiveness in encouraging critical thinking, diversity of perspectives, and measurable outcomes.
3. A guide on quality assurance criteria, including clarity, specificity, and alignment with user objectives.
4. Recommendations for tools and technologies to optimize prompt engineering, such as AI frameworks, datasets, and evaluation metrics.

Scope:
This task will involve:

• Developing prompts that encourage the language model to consider the economic, social, and environmental impacts of different systems and models.
• Incorporating real-world examples and case studies to provide context.
• Ensuring prompts are inclusive and avoid bias, adhering to ethical AI practices.

Objectives:

1. To create prompts that elicit coherent, well-structured, and insightful responses.
2. To ensure prompts are specific enough to guide the language model but open enough to allow for creative exploration.
3. To include measurable outcomes, such as response coherence, accuracy, and engagement rates.
4. To incorporate industry-standard terminology and metrics, such as KPIs for economic impact analysis.

Industry Best Practices and Standards:

• Adherence to ethical AI practices, including transparency, accountability, and fairness.
• Use of diverse datasets to avoid reinforcing biases.
• Inclusion of relevant technical specifications, such as model architecture and training data sources.
• Consideration of scalability and future maintenance, ensuring prompts can be expanded or modified without significant overhauls.

Quality Expectations:

• Prompts must be clear, specific, and aligned with user objectives.
• Responses should demonstrate a deep understanding of econo