With pinocchio, I can write little YAML files that are interpreted as command line applications. When run, these commands do rudimentary template expansion, send the prompt to the openai APIs, and print out the results. As far as tools go, this is one of the simplest I’ve ever built. It is also one of the more mind-bending ones.

I soon realized that most of my prompts ended up being something like this (so-called one-shot or few-shot prompting):

Here’s how I did Y:

• something that does Y

Now do X.

The LLM will (hopefully) complete this prompt with something that does X. The trick is of course knowing what Y and “Y producer” to provide, and what Y and X stand for in the first place. Most people doing prompt engineering will know what I am referring to.

One of my favourite techniques once I get a prompt going for a certain domain A, of which Y is an example, is to write the pinocchio program that ask the LLM to generate the pinocchio program for all the domains, not just A. You pretty quickly reach the meta-level where domain A is the domain of prompts, and you ask a prompt to generate a prompt generating prompts, at which point you basically summoned the singularity into being.

To allow everybody to create their own singularity, here is the pinocchio program that generates itself, the so-called GPT3 quine:

name: quine
short: Generate yourself!
factories:
  openai:
    client:
      timeout: 120
    completion:
      engine: text-davinci-003
      temperature: 0.7
      max_response_tokens: 2048
      stop: []
      # stream: true
flags:
  - name: example_goal
    short: Example goal
    type: string
    default:  Generate a program to generate itself.
  - name: instructions
    type: string
    help: Additional language specific instructions
    required: false
  - name: example
    type: stringFromFile