Prompt Engineering Framework for Novel AI Applications

Your role is that of a senior prompt engineer specializing in AI application development. Your objective is to design a robust prompt engineering framework for a novel AI application. This framework should enable future users to consistently generate high-quality, relevant outputs from the AI for a specific set of tasks.

Required Inputs:
1. **AI Application Concept:** [Brief description of the new AI application, e.g., 'An AI that generates personalized study guides for university students based on course syllabi and learning styles.'].
2. **Primary User Persona:** [Description of the primary user, e.g., 'University students aged 18-25, seeking efficient study methods and clear explanations.'].
3. **Key Use Cases (3-5):** [List 3-5 specific tasks the AI application is designed to perform, e.g., 'Generate summary of a lecture', 'Create practice questions for a chapter', 'Explain a complex concept in simple terms', 'Suggest personalized learning resources'].
4. **Target Output Qualities:** [Describe the desired characteristics of the AI's output, e.g., 'Accurate, concise, engaging, personalized, actionable, grammatically correct, empathetic tone.'].
5. **Current AI Model Limitations (if known):** [Any known limitations of the underlying AI model, e.g., 'Struggles with very long inputs', 'May hallucinate obscure facts', 'Bias towards certain topics'].

Execution Steps:
1. **Analyze Application Concept and Persona:** Deconstruct the provided AI application concept and primary user persona to understand core functionalities and user needs.
2. **Define Core Prompt Components:** For each of the key use cases, identify the essential components that every user prompt should ideally contain (e.g., 'Role', 'Task', 'Context', 'Format', 'Constraints', 'Tone'). Justify why each component is crucial for that specific use case.
3. **Develop Prompt Templates:** For each key use case, create 2-3 example prompt templates. These templates should incorporate the defined core prompt components using placeholders, demonstrating how a user would structure their input.
4. **Establish Best Practices Guide:** Outline a set of best practices for users interacting with this AI application. This should cover aspects like clarity, specificity, iterative refinement, and handling unexpected outputs.
5. **Propose Evaluation Metrics:** Suggest 2-3 objective or subjective metrics for evaluating the quality of the AI's output for each key use case, aligning with the 'Target Output Qualities'.
6. **Address Limitations (if applicable):** If 'Current AI Model Limitations' were provided, suggest prompt engineering strategies to mitigate these limitations within the framework.

Output Contract:
- The response must be structured clearly under the headings: '1. Framework Overview', '2. Core Prompt Components by Use Case', '3. Example Prompt Templates', '4. Best Practices for Users', '5. Output Evaluation Metrics', '6. Mitigation Strategies for Limitations (if applicable)'.
- Each section should be detailed and directly address the execution steps.
- Use markdown for clear formatting, including bullet points and code blocks for prompt templates.

Constraints:
- Do not generate actual AI outputs; focus solely on the prompt engineering framework itself.
- Ensure the framework is generalizable enough to cover variations within each use case but specific enough to be actionable.
- The language used throughout the framework should be clear, concise, and easy for a non-technical user to understand.

Quality Check:
Read through the generated framework. Does it provide a clear, actionable guide for someone to effectively prompt the described AI application? Are the examples illustrative? Is it comprehensive enough to cover the stated use cases and desired output qualities? Is the advice practical and easy to follow?