The biggest aha-moment with Copilot for me was when I wanted to implement tools for my GPT-based personal assistant. The function calling wasn’t yet available in the OpenAI API, and I’ve found that GPT-3.5 was really bad at using tools consistently in a long chat conversation. So I decided to implement a classifier DAG, with either a simple LLM prompt or a regular function in its nodes. Something like this:

what is this? (reminder | todo | other)
    reminder -> what kind of reminder? (one-time | recurring)
        one-time -> return the ISO timestamp and the reminder text in a JSON object like this
        recurring -> return the cron expression and the reminder text in a JSON object like this
    todo -> what kind of todo operation (add | delete | ...)
        ...
    other -> just respond normally

I wrote an example of using this classifier graph in code, something like this (it’s missing a lot of important details):

const decisionTree = new Decision(
  userIntentClassifier, {
    "REMINDER": new Decision(
      reminderClassifier, {
        "ONE_TIME": new Sequence(
          parseNaturalLanguageTime,
          createOneTimeReminder,
          explainAction
        ),
        "RECURRING": new Sequence(
          createRecurringReminder,
          explainAction
        ),
      }
    ),
    "TASK": new Decision(
      taskClassifier, {
        ...
      }
    ),
    "NONE": answerInChat,
  }
);

decisionTree.call(context);

And then I started writing class Decision, class Sequence, etc. and it implemented the classes perfectly!