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The Art of Prompt Engineering: A Guide to Crafting Effective Prompts for Language Models
Prompt engineering is both a science and an art. As language models like GPT-3 continue to explode in capability, properly structuring prompts has become one of the most important skills for anyone looking to tap into their potential.
In this post, I’ll provide a comprehensive overview of prompt engineering, starting with the basics before diving into more advanced techniques. By the end, you’ll have a toolkit of strategies you can use to craft prompts that consistently generate high-quality outputs.
Prompt engineering refers to the process of carefully designing the inputs you feed into a language model to produce a desired output.
At the most basic level, a prompt is just the text you provide to a language model as context for what you want it to generate. For example, inputting the text:
“Once upon a time in a kingdom far far away…”
Would prompt the model to continue the fairy tale story.
But not all prompts are created equal. Without guidance, language models can respond unpredictably or drift off topic. Prompt engineering is about structuring prompts to reliably steer the model towards high-quality responses.
The prompts you craft serve as “instructions” that tell the model:
With the right prompting strategies, you can dramatically improve the creativity, accuracy, and usefulness of the language model’s output.
Before diving into specific prompting approaches, let’s break down the key components that go into an effective prompt:
This refers to how you phrase the actual text used to prompt the model. The language should be clear, simple, and provide all the necessary context.
For example, “Write a children’s story about a lost puppy finding its way home” is an effective formulation.
Also called “few-shot examples”, these are 1-2 examples that demonstrate the type of output you want.
For the children’s story prompt, you could provide a short sample story to show the desired writing style.
Explicit instructions that tell the model exactly what you want it to generate, like “Write a 300 word blog post about prompt engineering”.
Additional instructions that define specifics like length, tone, or point-of-view. For example, “Write from the perspective of an AI assistant”.
Combining these components is what enables you to guide the model towards high-quality outputs. Now let’s look at some common prompt engineering patterns and techniques.
Here are some of the most effective prompt engineering strategies, along with examples:
Have the model complete a sentence, phrase, or paragraph:
“Jenny loved taking her dog Bruno to the park because…”
This prompts the model to provide a plausible completion.
Continue an existing story:
“Jenny took Bruno to the dog park every Sunday afternoon. Bruno loved being able to run around and play with the other dogs. One Sunday, when they arrived at the park, something seemed different. Usually the park was full of people and dogs playing, but today it wasempty. Bruno wandered around sniffing, confused about where everyone had gone. Suddenly, he heard…”
This prompts the model to continue the story in a sensible way.
Have the model answer a specific question:
“What is the capital of France?”
The model should respond with the correct answer – “Paris”.
Get a concise summary of a longer text:
“Here is a 600 word article about prompt engineering techniques. Please provide a 100 word summary:”
This prompts the model to distill the key points into a short summary.
Ask the model to assess the overall sentiment of a sample text:
“Here is a movie review: “The acting was wooden and the plot was convoluted. I wish I could get those 2 hours of my life back.” What is the overall sentiment of this movie review?”
The model should respond with something like “Very negative”.
Translate text into another language:
“Please translate the following Spanish text into English: “Me gusta aprender idiomas porque me permite conocer otras culturas.”
The model should provide an accurate English translation.
Have the model generate new text based on parameters:
“Write a 150 word blog post about prompt engineering, aimed at beginners.”
This prompts the model to generate a new blog post matching those criteria.
Generate poetry, stories, lyrics, etc:
“Write a short rhyming children’s poem about a dog playing in the park.”
The model will attempt to write creative text within those constraints.
As you can see from these examples, effective prompts clearly define the type of output required while providing necessary context and constraints. Now let’s look at some advanced techniques:
Once you grasp the basics, there are additional techniques you can use to further refine the model’s outputs:
Specify minimum/maximum word counts or number of sentences:
“Summarize this 2000 word article in 2 concise paragraphs of 3-4 sentences each.”
Controlling output length prevents rambling or overly truncated responses.
Describe the desired tone or writing style for the model to emulate:
“Explain quantum computing in simple, easy-to-understand language.”
This prompts a casual, conversational tone suited for beginners.
Give additional background information as context:
“A customer service agent needs to politely respond to an angry customer complaining about a defective product. The background is: The customer purchased a toaster oven 1 week ago and it is no longer working. Write the dialog between the agent and customer.”
This context helps the model craft an appropriate response.
Adopt a specific personality or point-of-view:
“Respond to the customer as if you are an empathetic, seasoned customer service agent who wants to resolve the issue.”
This guides the tone and approach the model should take.
Require specific words/phrases to be included:
“In 25 words or less, write a tagline for an ice cream shop that includes the words ‘scoops’, ‘flavors’, and ‘cones’.”
Adding must-include words forces creativity.
Prohibit unwanted content:
“Write a children’s story that does not include violence.”
This constrains the content to avoid inappropriate material.
Provide several examples to establish patterns:
[Example 1] “Roses are red, violets are blue…”
[Example 2] “The itsy bitsy spider climbed up the water spout…”
“Now write a short, simple rhyming poem for children using that style.”
Giving multiple examples better establishes the desired style.
As you can see, advanced prompt engineering focuses on providing constraints and guidelines to steer the model. Let’s now walk through a real hands-on example.
Let’s say I want the model to write a 150 word blog post that provides 3 tips for effective prompt engineering.
First, I’ll start with a simple formulation:
“Please write a 150 word blog post about prompt engineering tips.”
This provides the basic instructions, but the result may be overly generic. To improve it, I’ll add examples:
*”Here are 2 examples of prompt engineering tips:
Now write a 150 word blog post that provides 3 tips for effective prompt engineering.”*
This gives the model concrete examples to learn from. Next, I’ll provide additional background and constraints:
*”Prompt engineering is the process of carefully structuring the prompts used for language models like GPT-3 to get better results. It involves providing the right instructions, examples, and constraints.
Some characteristics of high-quality prompt engineering are:
Now, please write a 150 word blog post that provides 3 advanced prompt engineering tips for GPT-3 users. The post should have a conversational tone aimed at beginners.”*
With this richer context, the model can produce a much more tailored response. Prompt engineering takes practice, but following templated frameworks like this example can help you refine your skills.
To recap, here are some best practices to keep in mind:
Prompt engineering takes time and creativity, but following these principles will help you unlock the power of language models.
The key is providing just enough guidance to steer the model without overly constraining it. With practice, you’ll learn how to craft prompts tailored to your specific needs.
So in summary, treat prompt engineering as an iterative process. Analyze the results, identify areas for improvement, and continue refining your approach.
Hopefully this overview has provided a useful introduction to the art of prompt engineering. With these strategies, you can start crafting prompts that consistently generate high-quality outputs from language models like GPT-3.
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