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AI Prompts Used for LLaMA 2 Documentation

Model Overview and FAQ Prompt​

Prompt:

I need to create comprehensive documentation for Meta's LLaMA 2 model. Please generate:

1. A detailed FAQ section covering common questions about LLaMA 2
2. A technical overview suitable for developers
3. Key use cases and applications

Focus on:
- Model capabilities and limitations
- Technical specifications
- Practical implementation considerations
- Safety and ethical considerations

Please structure the output with clear headings and provide factual, technical information based on what you know about LLaMA 2.

Rationale: This prompt was designed to extract comprehensive information about LLaMA 2 while ensuring the AI provided structured, technical content suitable for developer documentation.

Performance Benchmarks Prompt​

Prompt:

Generate a detailed analysis of LLaMA 2's performance across different benchmarks and evaluation metrics. Include:

1. Specific benchmark scores for all model sizes (7B, 13B, 70B)
2. Comparison methodology
3. Evaluation criteria and datasets used
4. Performance analysis across different task types

Format this as a technical report section that would be suitable for a model card or technical documentation.

Rationale: This prompt focused on extracting specific performance data and evaluation methodologies to create accurate, data-driven documentation.

Risk Assessment Prompt​

Prompt:

Create a comprehensive risk assessment and limitations section for LLaMA 2 documentation. Cover:

1. Known biases and fairness concerns
2. Technical limitations and failure modes
3. Potential misuse scenarios
4. Mitigation strategies and safety measures
5. Monitoring recommendations

Write this in the style of responsible AI documentation, similar to what you'd find in academic model cards or industry best practices.

Rationale: This prompt was crafted to ensure comprehensive coverage of responsible AI considerations, which are critical for proper model documentation.

Prompt Engineering Considerations​

Prompt Design Strategy​

  • Specificity: Used specific, detailed requests rather than general questions
  • Structure: Explicitly requested structured output with headings and formatting
  • Context: Provided clear context about the intended use (technical documentation)
  • Scope: Defined clear boundaries for each prompt to avoid overlap

Iteration Process​

  1. Initial Broad Prompt: Started with general overview request
  2. Refinement: Broke down into specific technical areas
  3. Validation: Cross-referenced AI outputs with known information about LLaMA 2
  4. Supplementation: Used additional prompts to fill identified gaps

Quality Control Measures​

  • Fact-checking: Verified technical specifications against official sources
  • Consistency: Ensured consistent terminology and formatting across outputs
  • Completeness: Used multiple prompts to cover all required documentation sections
  • Accuracy: Manually reviewed and corrected any inaccuracies in AI-generated content