AI in ECU Research: Transparency

• Clarity - Ensure that each log entry clearly defines the purpose of the prompt, the context in which it is used, and the specific goals you aim to achieve. A well-defined prompt is essential for accurate AI output.
• Consistency - Keep track of each version of the prompt and any adjustments made over time. This ensures that prompts are used consistently, and you can easily track how each version affects the results.
• Comprehensiveness - Document all relevant details about the prompt, including input data, expected output, constraints, and additional parameters, so that others (or you in the future) can fully understand how the prompt was designed and executed.
• Change Tracking - Record any modifications made to the prompt and the reasons behind them. This ensures that the evolution of the prompt is well-documented and that you can see the impact of changes over time.
• Clarity of Outcome - Keep a clear record of the results generated by the AI, including any issues or unexpected outcomes. This ensures that the output can be evaluated and refined, as necessary.
• Collaboration and Continuity - Maintain a log that facilitates collaboration by ensuring others can easily follow the prompt’s evolution and reasoning. This encourages a transparent research process and ensures that prompt management is easily passed on to future team members or collaborators.

In the AI ecosystem, effective data management is crucial for achieving best outcomes. The better we manage and maintain our data, the better the results we can expect from our AI systems. Just like a healthy body requires regular care, attention, and the right habits, so does healthy data.

Data Preventative Care: Preventive measures are essential to maintaining data health. Regular backups and audits help safeguard data against loss and corruption. By implementing these practices, researchers can ensure that their data remains reliable and available for AI applications.

Data Emergency Care: Despite the best preventive measures, data issues can still arise. Data emergency care involves addressing and fixing broken, corrupted, or missing data promptly. Quick and effective responses to data emergencies are vital to minimise disruptions and maintain the integrity of AI systems.

Data Health Check-ups: Regular data assessments, cleaning, and optimisation are necessary to keep data in top condition. These health check-ups help identify and rectify issues before they become significant problems, ensuring that data remains accurate, relevant, and efficient for AI use.

By taking care of your data through these practices, you can ensure that it performs at its best, supporting the reliability of your research. Remember, a healthy AI ecosystem starts with well-managed data.