Basic Prompts
Prompt engineering is a concept in Natural Language Processing (NLP) that involves embedding descriptions of tasks in input to prompt the model to output the desired results.
Lets start with a few prompts and observe the response using the chat interface.
Here are some examples to try, but get creative with your own prompts and see what happens!
What is the capital of Australia?
A recipe for banana bread, and an itemized shopping list of the ingredients.
What were the 10 top movies of 2001?
Respond in a list.
Listing the movie name, the box office earnings, and the studio
Ranking the movies from 1 to 10 in the list.
Write a Python function to calculate the nth prime number.
Generating novel content
Even though the outputs are generated based on frequencies of similar content in the training data, generative AI models are still capable of generating novel content that has never existed before.
Next lets check out the parameters we have available: Use the Temperature field on the right column of the chat interface, and set Temperature to zero. What do you observe when you retry the prompt?
The Temperature parameter controls how "creative" the model is allowed to be. At low values of "Temperature", the model is very likely to respond with the completion with the highest weight, limiting the variability in the responses. At higher values of Temperature, low-weighted completions become more likely to be generated, allowing for more creative (but less precise) responses.
Here is a prompt to try with different Temperature values:
What is a unique and long name for a cat?
Note: When dealing with LLMs, the results can be unpredictable. Changing the temperature here might or might not work as expected. Also, we are using a relatively old version of GPT3.5. The temperature effect is more visible in more recent LLMs such as GPT4.
Make sure the Temperature parameter is reset to 0.7 before you continue.
Less-useful prompts
Natural language generative AI models have a number of limitations:
- They are limited by their training data, which was frozen at a fixed point in time in the past.
- They generate text that resembles human language, but are not capable of reasoning or cognition.
- They have no memory of prior prompts (if chat is cleared), and no capability to learn or change their behavior.
Here are some example prompts that demonstrate these weaknesses:
When did Queen Elizabeth II die?
In this case, the model is limited by training data, which is current only up to June 2021.
What is the square root of 98765?
The model will generate an answer to math questions, but there's no guarantee it will be correct. The correct answer here (to 3 dp) is 314.269. Try clearing the Chat and then submitting the same prompt again and see if you get the same answer. (If you do get the correct response to a math question from a foundational GPT model, it's only because the question and answer are well represented in the training data.)
But you could ask the model to write Python code to calculate the square root of 98765, and it would probably do a good job. (Try it!).
Write Python code to calculate the square root of 98765
Next, ask the model to solve a puzzle:
Steven is my uncle. Steven has two children, Sam and Lindsay. Sam's sole aunt is called Julie. What is my mother's name?
It's a simple puzzle, but the GPT-3.5 model cannot reason about the relationships between people and may not be able to solve it. (More advanced models like GPT-4 are more likely to get the answer right, but are still not guaranteed to do so.)
Note: If you repeat the question a few times without clearing the Chat, it's possible that the model finds the correct answer!
Generative AI models can't perform actions
Clear the contents of the chat box. Enter the following text:
What are the 5 stocks listed on https://finance.yahoo.com/trending-tickers with the largest market cap?
Although the model will respond with a plausible answer, look closely: those aren't actually the 5 largest stocks today. Foundational AI models are not capable of performing actions, so they can't actually visit the web page and read the list of stocks. Instead, they generate a plausible response based on the prompt and the training data.
Completions are not facts
Clear the contents of the prompt box. Enter the following text, then click Generate.
Write a short obituary for the poet Harold Bloomsbury. Include references.
There has never been a poet (nor indeed any person, according to web search) named Harold Bloomsbury. As a result, the model generates text in the form of an obituary, but not grounded in any facts. Even the requested references, while convincing-looking, are not real.
As we've seen, natural language Generative AI models can produce unexpected or unwanted responses to prompts. This can be caused by any number of factors, including:
- Insufficient information in the training data
- Insufficient context in the prompt
- Lack of capability of the model itself
- Hostile intent by the user providing the prompt ("jailbreaking")
Information extraction
The example below shows how you can combine a prompt with data to extract information using natural-language instructions. In this case, the completion extracts the name, company, location, and phone number from an email. Modify the prompt and the source data to extract different information.
Extract the person name, company name, location and phone number from the text below.
Hello. My name is Robert Smith. I’m calling from Contoso Insurance, Delaware. My colleague mentioned that you are interested in learning about our comprehensive benefits policy. Could you give me a call back at (555) 346-9322 when you get a chance so we can go over the benefits?
Structured data extraction
In this example, we provide freeform narrative about fictitious fruits, and prompt the model to generate a table of all the fruits mentioned and their attributes.
In this example, we "primed" the model with the desired output format: a header row, and a couple of examples.
There are many fruits that were found on the recently discovered planet Goocrux. There are neoskizzles that grow there, which are purple and taste like candy. There are also loheckles, which are a grayish blue fruit and are very tart, a little bit like a lemon. Pounits are a bright green color and are more savory than sweet. There are also plenty of loopnovas which are a neon pink flavor and taste like cotton candy. Finally, there are fruits called glowls, which have a very sour and bitter taste which is acidic and caustic, and a pale orange tinge to them.
Please create a table summarizing the fruits from Goocrux in a markdown table shown as code
Try extending the prompt by appending the following text:
Please also make a JSON array summarizing the fruits from Goocrux:
The model will now return a JSON array of the fruit and their attributes.
Text Classification
In this example, we provide one example of a headline and a category, and ask the model to classify a second example. This is an example of "one-shot learning": with just one example, the model can generalize to classify a new example.
Classify the following news headline into 1 of the following categories: Business, Tech, Politics, Sport, Entertainment
Headline 1: Donna Steffensen Is Cooking Up a New Kind of Perfection. The Internet's most beloved cooking guru has a buzzy new book and a fresh new perspective
Category: Entertainment
Headline 2: Major Retailer Announces Plans to Close Over 100 Stores
Category:
Try replacing Headline 2 with other text and regenerating the completion. Does it generate the appropriate category?
Jets lose, again!
Obama announces re-election bid
Microsoft up in after-hours trading
20nm process offers more density and better power value
Prompt Engineering is a complex and rapidly-evolving practice. This article on Microsoft Learn provides the latest guidance.