Engineering High-Level Prompts for AI | Science & Technology Insights

Enhanced Prompt: Crafting High-Level Prompts for AI in Science and Technology

Objective:
Design and implement advanced, context-aware prompts that leverage a language model's specialized knowledge in science and technology to generate high-quality, precise, and insightful responses. The goal is to ensure that AI outputs are not only accurate but also aligned with domain-specific expertise, scalability, and ethical considerations.

Deliverables:

1. A structured framework for crafting high-level prompts tailored for science and technology domains.
2. A list of best practices for prompt engineering, including the use of domain-specific terminology, structured query formats, and ethical considerations.
3. Guidelines for evaluating prompt quality, including metrics such as precision, recall, specificity, and engagement.
4. Recommendations for tools and technologies, such as large language models (LLMs) and AI frameworks, optimized for scientific and technological applications.
5. A section on addressing potential challenges, such as data biases, scalability, and maintaining future-proofing, with actionable solutions.

Industry Best Practices and Standards:

• Utilize large language models (LLMs) such as GPT-4, GPT-5, or ChatGPT with structured prompting techniques (e.g., Antonio's taxonomy, Fermi questions, or specific scientific inquiry frameworks).
• Incorporate domain-specific knowledge by integrating scientific databases, technical jargon, and industry-specific standards.
• Ensure ethical AI usage by adhering to guidelines such as AGI POD (AI governance principles) and avoiding unintended biases.
• Use version-controlled code and standardized templates for maintaining consistency and reproducibility.

Quality Expectations and Metrics:

• Precision: Ensure that AI responses are scientifically accurate and relevant to the query.
• Recall: Validate responses against domain-specific benchmarks or datasets to confirm comprehensive coverage.
• Specificity: Measure how prompts are tailored to the user's domain of expertise.
• Engagement: Assess the clarity and engagement of the AI response using NLP metrics like ROUGE or BLEU scores.

Technical Considerations: