It’s astounding to note that individuals with expertise in the emerging field of “Prompt Engineering for Beginners” can potentially earn up to $325K a year!
I am thrilled to present this complimentary guide focusing on prompt engineering for beginners. My ambition is that it will empower numerous people globally to acquire this novel skill, potentially opening up new employment opportunities.
What is Prompt Engineering?
Think of prompt engineering as teaching a robot. It’s about giving the right orders to super-smart computer models, called Large Language Models (LLMs), so they can do cool things.
These LLMs are amazing but can be a little tricky to handle. Prompt engineering is like a secret code to help us tell the LLMs exactly what you want them to do.
Essential Terminologies You Should Know
In the world of AI, a prompt is like a question or a command you give to a computer, and it replies based on what it has learned.
AI (Artificial Intelligence)
AI is like a smart robot or software that learns from its experiences and can do tasks that usually only humans can do.
This is a part of AI that helps computers learn automatically from their experiences. The computer gets better over time without us having to program every single step.
Natural Language Processing (NLP)
This is how computers learn to understand human language. It helps our devices read, understand, and make sense of the words we speak or type.
Think of this as a computer trying to work like a human brain. It’s a bunch of instructions that help the computer recognize patterns and make decisions.
A chatbot is a computer program that talks to us in our language. You can usually chat with them on apps, websites, or messaging platforms.
This is like a recipe for a computer. It’s a set of rules or instructions that helps a computer learn things on its own.
This is the information that you feed computers to help them learn. The better and more relevant the data, the better the computer can perform.
This is like fine-tuning a musical instrument but for a computer model. It involves adjusting parts of the model to make it perform better.
GPT (Generative Pretrained Transformer)
GPT is like a super-smart autocomplete. It was created by OpenAI and can predict what word comes next in a sentence to produce text that sounds like a human wrote it.
LLM (Large Language Model)
An LLM is a type of AI that’s been trained on lots of text. These models can write essays, answer questions, and even write code. They’re called “large” because they’re trained on a lot of data and can come up with many different responses.
Why is It a Big Deal?
Prompt engineering is super important because it lets us get the best out of these LLMs. Without it, LLMs might not behave the way you want them to.
By using prompt engineering, you can tell the LLMs exactly what to do, so you get the best results.
Who Should Learn About Prompt Engineering?
Anyone who’s interested in using these super-smart LLMs can learn about prompt engineering. This could be students like you, researchers, or people who create apps and websites.
If you want to use LLMs to write stories, answer questions, or solve tricky problems, then learning prompt engineering could be really useful!
The Basics of Prompt Engineering for Beginners
How to write a clear and concise prompt
A clear and concise prompt is a short piece of text that tells a language model what to do. It should be easy for the language model to understand, and it should avoid ambiguity.
Here are some tips for writing a clear and concise prompt:
- Use simple language. Avoid using jargon or technical terms that the language model may not understand.
- Be specific. The more specific you are, the better the language model will be able to understand what you are asking for.
- Use examples. If you can, provide examples of the type of output you are looking for. This can help the language model generate more relevant and accurate results.
- Keep it short. The shorter the prompt, the easier it will be for the language model to understand.
Here are some examples of clear and concise prompts:
- Write a poem about a cat.
- Generate a story about a robot who falls in love with a human.
- Solve the equation 2x + 2 = 6.
As you can see, these prompts are all clear and concise. They are easy to understand, and they avoid ambiguity.
Here are some examples of prompts that are not clear or concise:
- Generate a piece of creative text about a feline.
- Write a story about a robot who falls in love with a human in a way that is both heartwarming and thought-provoking.
- Solve the equation 2x + 2 = 6 in a way that is both creative and efficient.
These prompts are not clear or concise because they are too vague or ambiguous. They do not provide enough information for the language model to understand what is being asked.
How to provide context to a prompt
Context is information that provides the language model with a better understanding of what the prompt is asking for. This could include information about the topic, the style of writing, or the desired length of the output.
Here are some tips for providing context to a prompt:
- Start by identifying the main goal of the prompt. What do you want the language model to do? Generate a poem? Write a story? Solve a math problem?
- Once you know the main goal, you can start to provide context. This could include things like:
- The topic of the prompt.
- The style of writing you want the language model to use.
- The desired length of the output.
- Any other information that you think might be helpful?
- Be as specific as possible when providing context. The more specific you are, the better the language model will be able to understand what you are asking for.
- Use examples to illustrate your point. This can be helpful for language models that are not as familiar with the topic or style of writing you are asking for.
Here is an example of a prompt with context:
Write a poem about a cat who is lost. The poem should be in the style of Shel Silverstein and should be 10 lines long.
This prompt provides the language model with a lot of context. It tells the language model the main goal (write a poem), the topic (a cat who is lost), the style of writing (Shel Silverstein), and the desired length (10 lines). This should help the language model to generate a poem that is relevant, informative, and creative.
How to set constraints on a prompt
Constraints are restrictions that can be placed on the language model’s output. This could include things like the format of the output, the use of certain words or phrases, or the length of the output.
Here are some tips for setting constraints on a prompt:
- Identify the desired output. What do you want the language model to produce? A poem? A story? A code?
- Identify the constraints that you want to apply. What format do you want the output to be in? What words or phrases do you want to use? What length do you want the output to be?
- Express the constraints in the prompt. You can do this by using keywords or by providing examples.
- Be aware of the limitations of the language model. Not all language models are able to handle all types of constraints.
Here is an example of a prompt with constraints:
Write a poem about a cat in the form of a limerick. The poem should use the words “furry” and “meow” and should be 10 lines long.
This prompt sets three constraints on the language model’s output:
- The output must be a limerick.
- The output must use the words “furry” and “meow”.
- The output must be 10 lines long.
These constraints will help the language model to generate a poem that is more specific and relevant to the user’s request.
Examples of prompt engineering
Here are some examples of prompt engineering for writing a poem, answering a question, and generating code:
Writing a poem:
- Clear and concise prompt: “Write a poem about a cat.”
- Prompt with context: “Write a poem about a cat who is lost. The poem should be in the style of Shel Silverstein and should be 10 lines long.”
- Prompt with constraints: “Write a poem about a cat in the form of a limerick. The poem should use the words “furry” and “meow” and it should be 10 lines long.”
Answering a question:
- Clear and concise prompt: “What is the main ingredient in chocolate chip cookies?”
- Prompt with context: “I’m trying to bake chocolate chip cookies for a school project, could you tell me what the main ingredient is?”
- Prompt with constraints: “Can you tell me the main ingredient in chocolate chip cookies? It should be something that’s found in every kitchen.”
- Clear and concise prompt: “Can you generate a function to find the maximum of two numbers?”
- Prompt with context: “I’m creating a program that needs to pick the bigger number out of two given numbers. Could you help me write a function for that?”
- Prompt with constraints: “Could you write this function in Python, remember, the code should be simple and quick!”
- Clear and concise prompt: “Write a step-by-step guide on how to make a peanut butter and jelly sandwich.”
- Prompt with context: “I am a teacher and I need to give my students a step-by-step guide on how to make a peanut butter and jelly sandwich.”
- Prompt with constraints: “The guide must be written in a clear and concise way and must be easy for students to follow.”
- Clear and concise prompt: “Write a story about a cat who goes on an adventure.”
- Prompt with context: “I am writing a children’s book about a cat who goes on an adventure. I need the story to be exciting and engaging.”
- Prompt with constraints: “The story must be suitable for children under the age of 10 and must have a happy ending.”
- Clear and concise prompt: “Create a table that shows the population of the top 10 countries in the world.”
- Prompt with context: “I am writing a report on the world’s population. I need a table to show the population of the top 10 countries in the world, in 2020”
- Prompt with constraints: “The table must be in a tabular format and must show the country name, population, and population density.”
- Clear and concise prompt: “Generate a list of all the countries in the world with a population of over 100 million people.”
- Prompt with context: “I am working on a project to track the world’s most populous countries. I need a list of all the countries in the world with a population of over 100 million people.”
- Prompt with constraints: “The list must be sorted by population in descending order.
Tips for effective prompt engineering
The more specific your prompt is, the better the language model will be able to understand what you are asking. For example, instead of saying “Write a poem,” you could say “Write a poem about a cat who is lost.” This will help the language model to generate a more relevant and accurate poem.
Use the right format
If you want the language model to generate a specific type of output, such as a poem, a story, or a code, you should use the right format for your prompt. For example, if you want the language model to generate a poem, you should start your prompt with the word “poem.” This will help the language model to understand what you are asking and generate the output in the correct format.
Ask for the reasoning
If you want to understand the language model’s reasoning or if you want a more detailed response, you can ask the language model to explain its answer. For example, you could say “Write a poem about a cat who is lost, and then explain why you chose to write about a cat who is lost.” This will help you to learn more about how the language model works and how to use it effectively.
Experiment and learn
The best way to learn how to write effective prompts is to experiment and learn from your mistakes. Try different prompts and see what works best for you. You can also read about prompt engineering online or watch videos on YouTube. With a little practice, you’ll be writing effective prompts in no time!
Start with simple prompts
When you’re first starting out, it’s a good idea to start with simple prompts. This will help you to learn the basics of prompt engineering and to get a feel for how the language model works.
Keywords can help the language model to focus on the relevant information. For example, if you want the language model to generate a poem about a cat who is lost, you could use the keywords “cat,” “lost,” and “poem” in your prompt.
Context can help the language model to generate a more relevant and accurate response. For example, if you are writing a story about a cat who is lost, you could provide context by telling the language model where the cat is lost, what the cat looks like, and why the cat is lost.
Prompt engineering is a new and exciting field that is changing the way we interact with language models. By understanding how to write effective prompts, we can help language models to generate more relevant and accurate responses.
This can be used for a variety of tasks, such as answering questions, writing stories, and generating code.
The Future of Prompt Engineering
The future of prompt engineering is bright. As language models continue to improve, prompt engineers will need to develop new techniques to get the most out of them.
This could include using more complex prompts, setting constraints on the output, and using reinforcement learning to train language models to generate more desirable responses.
Resources for Learning More About Prompt Engineering
There are a number of resources available for learning more about prompt engineering. These include:
- The Prompt Engineering Guide: https://www.promptingguide.ai/
- The OpenAI Blog: https://openai.com/blog/
You may be also interested to read:
Top Prompt Engineering for Beginners Course Online