In the previous chapter, we discussed AI and how humans can instruct AIs to perform tasks. The process of instructing an AI to do a task is called prompting1. We tell the AI a set of instructions (the prompt) and it performs the task. Prompts can be as simple as a question, or as complex as multiple paragraphs.
Here are two examples of prompts:
1) Summarizing an Article
Say you are reading an article about snowfall in Florida. You want to quickly get the main ideas of the article, so you show the AI what you are reading, and ask for a summary2:
It is very rare for snow to fall in the U.S. state of Florida, especially in the central and southern portions of the state. With the exception of the far northern areas of the state, most of the major cities in Florida have never recorded measurable snowfall, though trace amounts have been recorded, or flurries in the air observed few times each century. According to the National Weather Service, in the Florida Keys and Key West there is no known occurrence of snow flurries since the European colonization of the region more than 300 years ago. In Miami, Fort Lauderdale, and Palm Beach there has been only one known report of snow flurries observed in the air in more than 200 years; this occurred in January 1977. In any event, Miami, Fort Lauderdale, and Palm Beach have not seen snow flurries before or since this 1977 event.
Summarize this paragraph in a single sentence:
Here is the AI's response. It is a much shorter read!
Snowfall is extremely rare in Florida, with the exception of the far northern areas,
and only one instance of snow flurries in the air has been recorded in the major cities
of Miami, Fort Lauderdale, and Palm Beach in the last 200 years.
2) Math Word Problem Solving
If you have a dataset of mathematical equations that you would like a language model to solve, you can create a prompt by posing the question "What is EQUATION".
For a given question, your full prompt could look like this:
What is 1,000,000 * 9,000?
For this prompt, GPT-3 (text-davinci-002) (an AI) sometimes answers 9,000,000 (incorrect). This is where prompt engineering comes in.
If, instead of asking
What is 1,000,000 * 9,000?, we ask
What is 1,000,000 * 9,000? Make sure to put the right amount of zeros, even if there are many:, GPT-3 will
answer 9,000,000,000 (correct). Why is this the case? Why is the additional specification
of the number of zeros necessary for the AI to get the right answer? How can we create
prompts that yield optimal results on our task? This last question, in particular,
is the focus of the field of Prompt Engineering, as well as this course.
Read on to learn how to engineer good prompts!
- Shin, T., Razeghi, Y., Logan IV, R. L., Wallace, E., & Singh, S. (2020). AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). https://doi.org/10.18653/v1/2020.emnlp-main.346 ↩
- This paragraph is from https://en.wikipedia.org/wiki/Snow_in_Florida↩