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Trick shot the incredible machine 33/15/2023 prefix, the model will sometimes decide to do something like add a blank line and then start an entirely new and unrelated list. If we simply stopped after line 4, without the 5. Starting a list item with a number (and in this case also a tag) forces the model to generate a new item.(A couple recent ones: “An online service that lets people rent out their parking or storage space to other people who need it.” and “An online service that lets you pay people who own drones to fly one over your house and take a picture.”) GPT-3 does a surprisingly good job at it, generating some genuinely interesting ideas.This prompt is interesting for a few reasons: The most likely token to come next in the document is a space, followed by a brilliant new startup idea involving Machine Learning, and indeed, this is what GPT-3 provides: “An online service that lets people upload a bunch of data, and then automatically builds a machine learning model based on that data.” (Ideas 1-4 are also based on ones suggested by GPT-3 from previous iterations of this prompt.) In this example prompt, we have some context ( This is a list of startup ideas:) and some few-shot examples. An online service that allows people to rent out their unused durable goods to people who need them. An online service that teaches children how to code.Ĥ. A website that lets you share a photo of something broken in your house, and then local people can offer to fix it for you.ģ. A website that lets you post articles you've written, and other people can help you edit them.Ģ. Here’s a sample few-shot (4-shot, technically) prompt:ġ. Few-shot: A prompt with one (1-shot) or more (n-shot, few-shot) examples.The name of a character from Lord of the Rings is: or [English: "Hello!", French: " Zero-shot: A prompt with no examples, e.g.Prompt: The text given to the language model to be completed.Model: The LLM being used, GPT-3 in this case.This document, called the “prompt”, often contains instructions and examples of what you’d like the LLM to do. A significant portion of this time was spent doing “Prompt Engineering”, in which you convince a Large Language Model (LLM) like GPT-3 that it is writing a document whose structure and content cause it to perform your desired task. I’ve been fortunate enough to get to spend time integrating GPT-3 into a complex product. Prompt Engineering allows developers to implement natural language understanding and soft decision-making processes that would otherwise be difficult or impossible. While I doubt traditional programming is going away anytime soon, I do predict that Prompt Engineering is going to be a very important part of most developers’ toolboxes. In this post I’ll briefly explain what Prompt Engineering is, why it matters, and some tips and tricks to help you do it well. The way you interact with GPT-3, or its forthcoming competitors, is through Prompt Engineering. I’m going to assume you know what GPT-3 is and why it’s a Big Deal™.
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