What if we wanted to analyze a small piece of text with no additional information or context and be able to get the most reasonable label that we wish to define for our own data? This can feed the more deterministic policy engines and rule engines, and even be a part of a larger context-driven analysis as required. OpenAI does provide a means to "content moderate" with preset classifications that can determine if your text belongs to one or more of the more vile categories. However, this analysis is more about how we can get more custom to defining our own labels against a given sentence or phrase.
We will look at 4 categories: viz. politics, PHI/PII, legal matters, and company performance. Given that we don't have the option of gathering probability scores from Open AI on such custom labels (at this point in time), we will try the more user-oriented prompt engineering route in Option 1 while Option 2 evaluates other pre-trained models from Hugging Face for the same.