Plan out some details of the inner workings of the platform

[nickumia]

Document this stuff (if necessary):

CORE QUALITIES

• contextual
• adaptive
• multi-modal
• real-time
• domain-specific

CORE COMPONENTS

• instruction
• context
• input data
• output indicatory (style)

PROMPT TECHNIQUES

• role-playing
• iterative refinement
• feedbackloops
• zero-shot
• few-shot
• chain-of-thought

CHALLENGES

• specificity vs. openness
• closed vs. open-ended q/a
• model complexity
• bias/fairness
• interdisciplinary collaboration

UN RESPONSIBLE METRICS

• https://unsceb.org/principles-ethical-use-artificial-intelligence-united-nations-system
• https://www.un.org/en/ai-advisory-body
• do no harm; defined purpose, necessity and proportionality; safety and security; fairness and non-discrimination; sustainability; right to privacy, data protection and data governance; human autonomy and oversight; transparency and explainability; responsibility and accountability; and inclusion and participation.

GOOGLE RESPONSIBLE METRICS

• https://ai.google/responsibility/responsible-ai-practices/
• FAIRNESS - Making AI helpful for everyone
• INTERPRETIBILITY - Making AI helpful for everyone
• PRIVACY -
• SAFETY AND SECURITY -

MS RESPONSBILE METRICS

• https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai?view=azureml-api-2
• FAIRNESS AND INCLUSIVENESS
• RELIABILITY/SAFETY
• TRANSPARENCY
• PRIVACY/SECURITY
• ACCOUNTABILITY

[nickumia]

Some notes from discussions:

• Technique_1__DB.pdf
• Prompt_Engineering_withMarginNotes.pdf