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Efficiently Reviewing Generative AI Code

1. Understand the Context​

  • Clarify the model's purpose and requirements.
  • Review the data sources and preprocessing steps.

2. Check Model Architecture​

  • Ensure the architecture matches the problem (e.g., transformer for text).
  • Look for modular, readable code.

3. Evaluate Training Pipeline​

  • Verify data splits (train/val/test).
  • Check for data leakage and reproducibility (random seeds, versioning).

4. Inspect Prompt Engineering​

  • Review prompt templates and input formatting.
  • Ensure prompts are robust and tested for edge cases.

5. Assess Evaluation Metrics​

  • Confirm use of appropriate metrics (BLEU, ROUGE, accuracy, etc.).
  • Look for human-in-the-loop or qualitative evaluations.

6. Review Code Quality​

  • Check for clear documentation and comments.
  • Ensure code follows style guides and best practices.

7. Test for Bias and Safety​

  • Look for bias mitigation strategies.
  • Review output filtering and safety checks.

8. Validate Deployment Readiness​

  • Inspect model packaging and API interfaces.
  • Ensure monitoring and logging are in place.

9. Run and Reproduce​

  • Execute code to verify results.
  • Check for reproducibility with provided instructions.

Tip: Use code review tools and automated tests to streamline the process.