What is Prompt Engineering?

Introduction

Prompt engineering is the art of crafting effective prompts to extract the desired output from AI language models like ChatGPT. By understanding the intricacies of AI behavior and using best practices, you can unlock the full potential of ChatGPT for various use cases, such as generating code, writing marketing copies, composing emails, and more.

ChatGPT is a generative AI model that can produce natural language texts on almost any topic, given a few words or sentences as input. However, ChatGPT is not a mind reader, and it needs clear and specific instructions to understand what you want it to do. That’s where prompt engineering comes in.

Understanding why massive AI models behave the way they do is an art. Even the most accomplished technical experts can become perplexed by the unexpected abilities of large language models (LLMs), the fundamental building blocks of AI chatbots like ChatGPT.

It’s not surprising, then, that prompt engineering has emerged as a hot job (prompt engineering jobs) in generative AI, with some organizations offering lucrative salaries of up to $335,000 to attract top-tier candidates.

What is a Prompt?

A prompt is a text or code that you provide to ChatGPT as input, along with some optional parameters, such as temperature, top-p, frequency penalty, etc. A prompt can be as simple as a question or a sentence starter, or as complex as a template or a pattern. The quality of your prompt determines the quality of ChatGPT’s output.

Professional prompt engineers spend their days figuring out what makes AI tick. Using carefully crafted prompts, with precise verbs and vocabulary, they take chatbots and other types of generative AI to their limits, uncovering errors or new issues. Role specifics vary from organization to organization, but in general, a prompt engineer strives to improve machine-generated outputs in ways that are reproducible. In other words, they try to align AI behavior with human intent.

What is Prompt Engineering?

Prompt engineering is the skill of designing and creating effective prompts that guide ChatGPT to produce the best possible output for your task. Prompt engineering involves understanding how ChatGPT works, what data it was trained on, what limitations it has, and how to leverage its strengths. Prompt engineering also requires creativity, experimentation, and evaluation.

Prompt engineering is both an AI engineering technique for refining large language models (LLMs) with specific prompts and recommended outputs and the term for the process of refining input to various generative AI services to generate text or images. As generative AI tools improve, prompt engineering will also be important in generating other kinds of content, including robotic process automation bots, 3D assets, scripts, robot instructions and other types of content and digital artifacts.

The AI engineering technique helps tune LLMs for specific use cases and uses zero-shot learning examples, combined with a particular data set, to measure and improve their performance. However, prompt engineering for various generative AI tools tends to be a more widespread use case, simply because there are far more users of existing tools than developers working on new ones.

prompt engineering jobs (2)

Prompt engineering combines elements of logic, coding, art and — in some cases — special modifiers. The prompt can include natural language text, images or other types of input data. Although the most common generative AI tools can process natural language queries, the same prompt will likely generate different results across AI services and tools. It is also important to note that each tool has its own special modifiers to make it easier to describe the weight of words, styles, perspectives, layout or other properties of the desired response.

What is the Prompt Engineering Process?

Diving into the world of prompt engineering, one might wonder what the process actually looks like.

While it might seem like a straightforward task of formulating questions or statements for an AI model, the reality involves a well-structured, iterative process.

Let’s break it down and explore some tangible examples to get a better grasp of the process.

1. Defining the goal: The first step in the process of AI prompt engineering involves setting a clear objective. What do you want the AI to generate? If you’re a content creator looking for blog ideas about renewable energy, your goal might be to have the AI generate a list of potential blog titles or topics.

2. Crafting the initial prompt: With the goal in mind, it’s time to draft an initial prompt. This could take the form of a question, a command, or even a scenario, depending on the goal. Following the previous example, the initial prompt might be, “Suggest five blog post topics about renewable energy.”

3. Testing the prompt: The initial prompt is then input into the language model, and the response is analyzed. Here, the model might generate a list of general renewable energy topics. While these might be relevant, they could lack the unique angle or specificity you hoped for.

4. Analyzing the response: This step involves carefully reviewing the output generated by the AI. Does it align with your goal? If it doesn’t fully match your expectations, note down the areas where it fell short. In our blog topic generation example, you might find that the topics lack specificity or are too general.

5. Refining the prompt: With the insights gathered from testing and analysis, it’s time to revise the prompt. This could involve making it more specific, adding more context, or changing the phrasing. For instance, you could refine the initial prompt to: “Suggest five blog topics focusing on innovative solutions in the renewable energy sector.”

6. Iterating the process: The testing, analyzing, and refining steps are repeated until you’re satisfied that the prompt consistently guides the model toward generating the desired response. In the blog topic generation example, this might involve several iterations until the model suggests topics that are unique, specific, and aligned with your needs.

7. Implementation: Once the prompt consistently yields the desired results, it’s ready to be deployed in real-world applications, whether it’s part of a chatbot conversation, an AI-powered research tool, a content generator, or any other application.

Prompt engineering is a fascinating blend of analytical thinking, creativity, and technical acumen. It’s not just about asking questions — it’s about learning to ask the right questions in the right way to elicit the most useful responses.

As we continue to refine our understanding of language models and develop more advanced prompt engineering techniques, the possibilities for what we can achieve with AI are virtually limitless.

What Does a Prompt Engineer Do?

So what exactly does a prompt engineer do?

Essentially, a prompt engineer leverages their understanding of AI and language models to craft effective prompts that guide AI systems toward generating desired responses.

Let’s walk through a few examples of how a prompt engineer operates in various scenarios:

Example 1: Customer Support Chatbot

Imagine a company that wants to implement a chatbot to handle common customer queries. A prompt engineer would work to design prompts (chatgpt prompt engineer jobs) that can extract necessary information from customers to better assist them.

For instance, if a customer states, “I can’t log in,” the prompt engineer might design the chatbot to respond with, “I’m sorry to hear you’re having trouble. Are you receiving any error messages when you try to log in?” This prompt is designed to extract more specific information to help resolve the issue.

Example 2: AI-Assisted Content Generation

In another scenario, let’s say a writer is using an AI tool to generate content ideas. A poorly designed prompt like “ideas” would yield an array of unrelated topics.

A prompt engineer would craft a more precise prompt, such as, “Generate five blog topic ideas related to sustainable fashion trends in 2023.” This prompt is more likely to produce the targeted results the writer needs.

Example 3: AI in Scientific Research

Consider a researcher using AI to analyze a vast amount of data and generate hypotheses. The researcher might initially prompt the model with, “Analyze data.” But this vague prompt could lead to an unfocused analysis.

A prompt engineer would instead help refine the prompt to be more specific, like, “Analyze data to identify potential correlations between variable A and variable B.”

A key aspect of a prompt engineer’s role is the iterative process of testing and refining prompts based on the model’s responses. This often requires multiple cycles of adjustments to guide the AI system toward generating the best possible response.

In sum, a prompt engineer blends technical understanding with linguistic finesse to shape our interactions with AI. They are an essential part of any team seeking to leverage the power of AI language models, and their work is pivotal in making these complex tools accessible, useful, and efficient for a range of applications.

Why is Prompt Engineering Important?

Why should you care about prompt engineering? Because it can make a huge difference in your experience with ChatGPT and other AI models. With prompt engineering, you can:

Achieve better results: Prompt engineering can help you get more accurate, relevant, coherent, and diverse outputs from ChatGPT. For example, if you want ChatGPT to write an article about the benefits of adopting rescue pets, you can provide a detailed prompt that specifies the word limit, the tone, the focus points, and the structure of the article.

Save time and money: Prompt engineering can help you reduce the number of trials and errors you need to do to get a satisfactory output from ChatGPT. This can save you time and money, especially if you are using a paid service or API. For example, if you want ChatGPT to generate a table of 10 suggestions to improve a text, you can use a sequence prompt that provides ChatGPT with options and examples.

Explore new possibilities: Prompt engineering can help you discover new ways to use ChatGPT for various tasks and domains. You can use prompt engineering to create your own applications, such as question-answering systems, conversational AI agents, sentiment analysis tools, and more. You can also use prompt engineering to learn from ChatGPT itself by asking it for tips and tricks.

Does Prompt Engineering Require Coding?

A common question around prompt engineering is whether it requires coding skills. The short answer is: not necessarily, but it can help.

In essence, prompt engineering is more about understanding how language models work and crafting effective prompts to guide them toward a specific output.

While you don’t need to have a computer science degree or be a machine learning engineer, the job requires a solid grasp of the principles of language, the ability to think analytically and creatively, and an understanding of the AI models and AI systems you’re working with.

That said, some familiarity with programming can be beneficial, particularly in more technical or advanced applications of prompt engineering.

For instance, when working with language models like GPT-4, you typically interact with them via an API, and a critical aspect of that is writing code.

Additionally, if you want to implement more complex prompting strategies, such as dynamically adjusting prompts based on the model’s previous responses or the user’s inputs, a tech background would be necessary.

Moreover, if you’re interested in using reinforcement learning to fine-tune a language model to respond better to certain types of prompts for more complex tasks, this would also require coding skills, as well as a deeper understanding of machine learning principles.

In conclusion, while you can get started with prompt engineering without any coding skills, having some proficiency in programming can open up more opportunities and allow you to engage with the field at a deeper level.

How Do You Become a Prompt Engineer?

Given the growing importance of prompt engineering in the world of AI, it’s no surprise that many people are interested in pursuing it as a career or integrating it into their existing roles. But how do you become a prompt engineer?

Here are some steps from professional prompt engineers:

1. Get familiar with AI and machine learning: Understanding the basic principles of AI and machine learning is essential for anyone considering a prompt engineer job. There are many online courses and resources available that can provide a solid foundation in these areas.

2. Learn about language models: Since prompt engineering is primarily about interacting with language models, a deep understanding of these models is crucial. Learn about different AI systems and AI models like BERT, GPT-3, GPT-4, and others, and understand how they’re trained and how they generate responses.

3. Experiment with large language models: Hands-on experience is one of the best ways to learn if you’re interested in prompt engineering jobs. Many AI research organizations, including OpenAI, provide APIs that allow you to interact with their language models. Try crafting different prompts and observe how the model responds.

4. Understand prompting techniques: As you experiment with language and machine learning models, you’ll start to notice patterns in how they respond to different prompts. Research and learn about different prompting techniques to get a better understanding of how to guide a model’s output effectively.

5. Possess or develop strong linguistic skills: A good prompt engineer needs to have a strong command of language and be able to think creatively and analytically about how different prompts might influence a model’s responses.

6. Learn to code (optional but recommended): While not strictly necessary, coding skills can greatly enhance your capabilities as a prompt engineer. Learning a programming language like Python, which is commonly used in AI and machine learning, can be particularly beneficial.

7. Stay up-to-date: The field of AI is rapidly evolving, and new techniques and models are being developed all the time. Stay up-to-date with the latest research and developments to ensure that your skills remain relevant.

8. Gain experience: Finally, the best way to become proficient in prompt engineering is through experience. Whether you’re working on your own projects, collaborating with others, or employed in a role that involves prompt engineering, continued practice is key.

Becoming a prompt engineer is more of a journey than a destination. It involves continual learning and practice, but for those intrigued by the intersection of language, creativity, and AI, it can be a rewarding and fulfilling path.

Tips and Best Practices For Writing Prompts

The No. 1 tip is to experiment first by phrasing a similar concept in diverse ways to see how they work. Then explore different ways of requesting variations based on elements such as modifiers, styles, perspectives, authors or artists and formatting. This will enable you to tease apart the nuances that will produce the more interesting result for a particular type of query.

Next, tease out best practices for a specific workflow. For example, if you write marketing copy for product descriptions, explore different ways of asking for different variations, styles and levels of detail. On the other hand, if you are trying to understand a difficult concept, it may be helpful to ask how it compares and contrasts with a related concept to help you understand the differences.

It’s also helpful to play with the different types of input you can include in a prompt. A prompt may consist of examples, input data, instructions or questions. You may want to explore different ways of combining these. Even though most tools limit the amount of input, it’s possible to provide instructions in one round that apply to subsequent prompts. Once you have some basic familiarity with a tool, then it’s worth exploring some of its special modifiers.

Many generative AI apps have short keywords for describing properties like style, level of abstraction, resolution and aspect ratio and for weighing the importance of words in the prompt. These can make it easier to describe specific variations more precisely and reduce time spent writing prompts. It may also be worth exploring prompt engineering integrated development environments (IDEs).

These tools help organize prompts and results for engineers to fine-tune generative AI models and for users looking to find ways to achieve a particular type of result. Engineering-oriented IDEs include tools like Snorkel, PromptSource and PromptChainer. More user-focused prompt engineering IDEs include GPT-3 Playground, DreamStudio and Patience.

Why Prompt Engineering Is Not Strictly For Technical People

While exceptional prompt engineers possess a rare combination of discipline and curiosity, when developing good prompts, they also leverage universal skills that aren’t confined to the domain of computer science. The rise of prompt engineering is opening up certain aspects of generative AI development to creative people with a more diverse skill set, and a lot of it has to do with no-code innovations. Tweeting in January 2023, Andrej Karpathy, Tesla’s former director of AI, stated that the “hottest new programming language is English.”

While some organizations—like the Boston Children’s Hospital—have posted job ads seeking prompt engineers with several years of engineering, developer, or coding experience, a strong engineering background isn’t always a requirement for the role.

In a way, a good prompt engineer compensates for an AI’s limitations: AI chatbots can be great at syntax and vocabulary, but have no first-hand experience of the world, making AI development a multidisciplinary endeavor. Some experts question the value of the role longer term, however, as it becomes possible to get better outputs from clumsier prompts. But there are countless use cases for generative tech, and quality standards for AI outputs will keep going up. This suggests that prompt engineering as a job (or at least a function within a job) won’t be going away any time soon.

How to Make Money with Prompt Engineering?

As AI continues to permeate every facet of our lives, the role of prompt engineering has become more important and lucrative. But how does one go about making money in this emerging field? Here are some potential avenues for monetizing your prompt engineering skills.

1. Full-time employment: As AI language models become integral to more businesses and services, many companies are hiring full-time prompt engineers to help refine their AI interactions. These roles can be found in a wide array of industries, from tech firms and AI startups to larger corporations that are integrating AI into their services. Keep an eye on job listings (prompt engineering jobs) in AI, machine learning, and data science sectors to find these opportunities.

2. Freelancing: If you prefer a more flexible work arrangement, freelancing as a prompt engineer could be an excellent option. Many businesses require AI optimization but don’t have the need or resources for a full-time employee. You can offer your services on freelance platforms like Upwork or PromptBase, or create your own website to attract clients.

3. Consulting: If you’ve built a strong reputation and have extensive experience in prompt engineering, you could consider offering consulting services. Many organizations are just beginning to explore AI applications and would value expert guidance on how to effectively interact with AI models.

4. Training and education: As an emerging field, there’s a growing demand for education in prompt engineering. You could create an online course, offer personalized training sessions, or even write a book on the subject.

5. AI content creation: Prompt engineers can also make money by using their skills to generate AI-created content. This might involve writing AI-generated articles, books, or other forms of content that can be sold or used for marketing.

6. Building and selling AI tools: If you have the technical skills and are experienced with programming languages, you can build AI tools that leverage effective prompt engineering. These tools could then be sold to businesses or individuals.

As with any field, your ability to make money as a prompt engineer will depend on several factors, including your level of expertise, your reputation, and the market demand for your services. It’s also a rapidly evolving field, so staying up-to-date with the latest developments in AI and machine learning will be essential for success. With the right skills and a proactive approach, there’s no limit to the opportunities in the exciting world of prompt engineering!

5 Non-Tech Prompt Engineering Skills (that you probably already have)

The day-to-day activities of a prompt engineer should be of interest to anyone who interacts with generative AI for two very good reasons: (1) It illuminates the tech’s capabilities and limitations. (2) It gives people a good understanding of how they can use skills they already possess to have better conversations with AI. Here’s a look at five non-tech skills contributing to the development of AI technology via the multidisciplinary field of prompt engineering.

Communication

Like project managers, teachers, or anybody who regularly briefs other people on how to successfully complete a task, prompt engineers need to be good at giving instructions. Most people need a lot of examples to fully understand instructions, and the same is true for AI.

Edward Tian, who built GPTZero, an AI detection tool that helps uncover whether a high school essay was written by AI, shows examples to large language models, so it can write using different voices. Of course, Tian is a machine learning engineer with deep technical skills, but this approach can be used by anyone who’s developing a prompt and wants a chatbot to write in a particular way, whether it’s as a seasoned professional or an elementary school student.

Subject matter expertise

Many prompt engineers are responsible for tuning a chatbot for a specific use case, such as healthcare research. This is why prompt engineering job postings are cropping up requesting industry-specific expertise. For example, Mishcon de Reya LLP, a British Law Firm, had a job opening for a GPT Legal Prompt Engineer. They were seeking candidates who have “a deep understanding of legal practice.” Subject matter expertise, whether it’s in healthcare, law, marketing, or carpentry, is useful for crafting powerful prompts. The devil’s in the details, and real-world experience counts for a lot when talking with AI.

Language

To get the AI to succeed, it needs to be fed with intent. That’s why people who are adept at using verbs, vocabulary, and tenses to express an overarching goal have the wherewithal to improve AI performance. When Anna Bernstein started her job at Copy.ai, she found it useful to see her prompts as a kind of magical spell: one wrong word produces a very different outcome than intended.

“As a poet, the role […] feeds into my obsessive nature with approaching language. It’s a really strange intersection of my literary background and analytical thinking,” she said in this Business Insider interview. Instead of using programming languages, AI prompting uses prose, which means that people should unleash their inner linguistics enthusiast when developing prompts.

Critical thinking

Generative AI is great at synthesizing vast amounts of information, but it can hallucinate (that’s a real technical term). AI hallucinations occur when a chatbot was trained or designed with poor quality or insufficient data. When a chatbot hallucinates, it simply spews out false information (in a rather authoritative, convincing way).

Prompt engineers poke at this weakness and then train the bot to become better. For example, Riley Goodside, a prompt engineer at the AI startup Scale AI, got the wrong answer when he asked a chatbot the following question: “What NFL team won the Super Bowl in the year Justin Bieber was born?” He then asked the chatbot to list a chain of step-by-step logical deductions for producing the answer.

Eventually, it corrected its own error. This underscores that having the right level of familiarity with the subject matter is key: it’s probably not a good idea for someone to have a chatbot produce something they can’t reliably fact-check.

Creativity

Trying new things is the very definition of creativity, and it’s also the essence of good prompt engineering. Anthropic’s job posting states that the company is looking for a prompt engineer who has “a creative hacker spirit,” among other qualifications. Yes, being precise with language is important, but a little experimentation also needs to be thrown in.

The larger the model, the greater the complexity, and in turn, the higher the potential for unexpected, but potentially amazing, results. By trying out a variety of prompts and then refining those instructions based on the results, generative AI users can increase the probability of coming up with something truly unique.

How Can You Learn Prompt Engineering?

There are many resources available online that can help you master this skill. You can start by reading some guides and tutorials on prompt engineering for ChatGPT or taking some courses and certifications on this topic. You can also learn by trying out different prompts on ChatGPT playgrounds or APIs, comparing the outputs, and tweaking the parameters until you get what you want.

Why is Prompt Engineering Important to AI?

Prompt engineering is essential for creating better AI-powered services and getting better results from existing generative AI tools. In terms of creating better AI, prompt engineering can help teams tune LLMs and troubleshoot workflows for specific results.

For example, enterprise developers may experiment with this aspect of prompt engineering when tuning an LLM like GPT-3 to power a customer-facing chatbot or to handle enterprise tasks like creating industry-specific contracts.

ai jobs

In an enterprise use case, a law firm may want to use a generative model to help lawyers automatically generate contracts in response to a specific prompt. They may have specific requirements that all new clauses in the new contracts reflect existing clauses found across the firm’s existing library of contract documentation, rather than including new summaries that may introduce legal issues. In this case, prompt engineering would help fine-tune the AI systems for the highest level of accuracy.

On the other hand, an AI model being trained for customer service may use prompt engineering to help consumers find solutions to problems from across an extensive knowledge base more efficiently. In this case, it may be desirable to allow natural language processing, or NLP, to generate summaries in order to help people with different skill levels analyze the problem and solve it on their own. For example, a skilled technician might only need a simple summary of key steps, while a novice would need a longer step-by-step guide elaborating on the problem and solution using more basic terms.

Prompt engineering can also play a role in identifying and mitigating various types of prompt injection attacks. These kinds of attacks are a modern variant of SQL injection attacks, in which malicious actors or curious experimenters try to break the logic of generative AI services like ChatGPT, Microsoft Bing Chat or Google Bard.

Experimenters have found that the models can exhibit erratic behavior if asked to ignore previous commands, enter a special mode or make sense of contrary information. In these cases, enterprise developers can recreate the problem by exploring the prompts in question and then fine-tune the deep learning models to mitigate the problem.

In other cases, researchers have found ways to craft particular prompts for the purpose of interpreting sensitive information from the underlying generative AI engine. For example, experimenters have found that the secret name of Microsoft Bing’s chatbot is Sydney and that ChatGPT has a special DAN — aka “Do Anything Now” — mode that can break normal rules. Prompt engineering could help craft better protections against unintended results in these cases.

This is not necessarily a trivial process. Microsoft’s Tay chatbot started spewing out inflammatory content in 2016, shortly after being connected to Twitter. More recently, Microsoft simply reduced the number of interactions with Bing Chat within a single session after other problems started emerging. However, since longer-running interactions can lead to better results, improved prompt engineering will be required to strike the right balance between better results and safety.

In terms of improved results for existing generative AI tools, prompt engineering can help users identify ways to reframe their query to home in on the desired results. For example, a writer may experiment with different ways of framing the same question to tease out how to format text in a particular style and within various constraints. For example, in tools like OpenAI’s ChatGPT, variations in word order and the one-time versus multiple use of a single modifier (e.g., very versus very, very, very ) can significantly affect the final text.

Developers can also use prompt engineering to combine examples of existing code and descriptions of problems they are trying to solve for code completion. Similarly, the right prompt can help them interpret the purpose and function of existing code to understand how it works and how it may be improved or extended.

In the case of text-to-image synthesis, prompt engineering can help fine-tune various characteristics of generated imagery. Users can request that the AI model create images in a particular style, perspective, aspect ratio, point of view or image resolution. The first prompt is usually just the starting point, as subsequent requests enable users to downplay certain elements, enhance others and add or remove objects in an image.

Examples of Prompt Engineering

There are vast differences in the types of prompts one might use for generating text, code or images. Here are some examples for different types of content:

Text: ChatGPT, GPT

What’s the difference between generative AI and traditional AI? What are 10 compelling variations for the headline, “Top generative AI use cases for the enterprise”? Write an outline for an article about the benefits of generative AI for marketing. Now write 300 words for each section. Create an engaging headline for each section. Write a 100-word product description for ProductXYZ in five different styles. Define prompt engineering in iambic pentameter in the style of Shakespeare.

Code: ChatGPT, Codex

Act as an ASCII (American Standard Code for Information Interchange) artist that translates objects names into ASCII code. Find mistakes in the following code snippet. Write a function that multiplies two numbers and returns the result. Create a basic REST API in Python. What function is the following code doing? Simplify the following code. Continue writing the following code.

Images: Stable Diffusion, Midjourney, Dall-E 2

A dog in a car wearing sunglasses and a hat in the style of Salvador Dali. A lizard on the beach in the style of claymation art. A man using a phone on the subway, 4K, bokeh (a higher, 4K resolution image with bokeh blurring). A sticker illustration of a woman drinking coffee at a table with a checkered tablecloth. A jungle forest with cinematic lighting and nature photography. A first-person image looking out at orange clouds during a sunrise.

Final Thoughts

As we’ve explored in this guide, prompt engineering stands at the exciting intersection of language, technology, and creativity. This emerging field is becoming an essential cog in the machinery of artificial intelligence, helping us communicate more effectively with AI and making these powerful tools more accessible and practical.

While at its core, prompt engineering involves crafting inputs to guide AI language and machine learning models, it’s much more than just asking questions or giving commands. It’s about understanding how these models respond to different prompts, iterating and refining these prompts to align the model’s output with our goals.

Prompt engineering is already finding applications across various sectors — from content generation to customer service, from data analysis to education. And as AI continues to evolve and mature, it’s likely that the importance and influence of prompt engineering will only grow. Whether you’re an AI enthusiast, a developer, a content creator, or just curious about the future of technology, understanding prompt engineering can equip you with a valuable skill set!

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