How to Become a Prompt Engineer: Duties, Skills, and Steps

Key takeaways

To become a prompt engineer, you should reflect on your career goals, earn relevant credentials, build specific technical and writing skills, gain hands-on experience, and apply for roles in your desired industry.

• Becoming a prompt engineer involves a five-step process of identifying your goals, earning credentials such as a bachelor's degree or certification, building skills in areas like writing and machine learning, gaining experience through personal projects, and applying for jobs.

• Prompt engineers must master specific techniques including zero-shot, few-shot, chain of thought, and knowledge generation prompting to optimize the output of large language models.

• You can practice using a variety of AI language models and design your own projects, such as creating chatbots, to hone your skills.

Keep reading to learn more about the duties and future of this emerging field. Build your professional knowledge by enrolling in the Prompt Engineering Specialization.

What is prompt engineering?

Prompt engineering is the process of optimizing the output of large language models like ChatGPT or Google Gemini by crafting input prompts that help them generate the desired output.

Prompt engineering is all about asking a language model better questions, providing better instructions, and even assigning it a role so that it generates the output you want, such as a list of blog topics, product descriptions, and coding documentation. Good prompts connect what a human wants to create with what a machine can generate.

Prompt engineering is a real job, though it is continuing to evolve along with advancements in AI. In 2026, there are many paths you can take to become a prompt engineer.

Prompt engineer salary

The amount of experience you have will impact your earnings. Here are average prompt engineering salaries by experience [1]:

Entry-level salary: $87,000-139,000

Mid-career salary: $100,000-$164,000

Senior salary: $129,000-224,000

The industry you work in will also impact your earnings. Here are average prompt engineer salary ranges by industry [1]:

Biotechnology & pharmaceuticals: $97,000-$154,000

Management & consulting: $103,000-$169,000

Aerospace: $105,000-$164,000

Agriculture: $113,000-$188,000

Information Technology: $117,000-$168,000

Media & communication: $140,000-$224,000

Bloomberg reports that some prompt engineering jobs can command salaries as high as $335,000 [2]. In 2026, Glassdoor reports that the average prompt engineer earns $128,000/year, with top earnings receiving salaries as high as $165,000 [3].

Did you know this about prompt engineering?

Prompt engineering is primarily used with text-to-text models, meaning that text comprises the input (prompt) and output. Other models, like text-to-audio and text-to-image, allow prompt engineers to input text and have the model produce audio files or images.

What does a prompt engineer do?

Prompt engineers are also referred to as AI (artificial intelligence) prompt engineers or large language model (LLM) prompt engineers. It is a fairly new field that emerged alongside generative AI. They can work in industries as varied as marketing, education, finance, human resources, and health care.

As a prompt engineer, you’ll be responsible for:

• Crafting AI prompts to get desired outputs

• Testing and analyzing outputs from the AI by experimenting with different prompts

• Considering the ethics, cultural sensitivity, fairness, and bias involved with a prompt and the output it enables

• Using your human judgment to identify what’s lacking in AI-generated output and then refining prompts to optimize output

• Embedding AI prompts into applications and software for use in automating complex or repetitive tasks

• Working on cross-functional teams to develop products

• Integrating AI chatbots into a team’s workflow

• Monitoring AI systems’ performance

Do I need to know how to code to become a prompt engineer?

Whether you need to know how to code to become a prompt engineer in 2026 depends on the kind of role you're targeting. Linguistic or creative prompt engineering roles are often considered part of marketing, content design, or UX writing departments. Their primary tools are natural language and psychological frameworks; these roles prioritize clarity, empathy, and context to guide AI output. However, if your goal is to be hired into AI workflow or LLM engineer roles, then coding is required. In these technical positions, you’ll define and build workflows, develop retrieval-augmented generation (RAG) pipelines, manage model performance, and ensure data security. While you may be able to find "no-code" roles, the highest-paying jobs increasingly require the ability to treat prompts as structured code that must be version-controlled, tested, and scaled.

What’s the future of prompt engineering?

This field is still new, so it may be too soon to accurately predict what prompt engineering will look like in the near future and beyond.

On the one hand, quality standards for LLM outputs will become higher, according to Zapier, so prompt engineers will need better skills [1]. On the other hand, an article in the Harvard Business Review suggests that “AI systems will get more intuitive and adept at understanding natural language, reducing the need for meticulously engineered prompts” [2].

Prompt engineering roles by industry

Prompt engineering tasks vary depending on industry. You’ll find the role of prompt engineer is shaped by the larger organization you work for.

Some examples of prompt engineer tasks by industry include:

Healthcare and Biotech: Engineers in this sector can facilitate streamlined patient schedule, record keeping, and billing [4]. They can also leverage prompting to review research, analyze data and generate hypotheses. 

Finance and Legal Services: Professional prompt engineers can architect prompts to review transactions, analyzing contract, or generate compliance reports. In these fields where compliance is paramount, engineers need to ensure AI behavior is operationally safe and auditable.

Marketing and Creative Industries: In these sectors, prompt engineers craft prompts that capture a brand's tone and voice across formats like text-to-image and text-to-video. Beyond content creation, they can write and optimize copy, and analyze data about customer behaviors.

Education and EdTech: Roles in education focus on personalizing learning through "adaptive prompting," where AI tutors are engineered to adjust their mentoring style based on individual student data. Prompts can also produce lesson plans and rubrics.

How to become a prompt engineer: 5 steps 

Prompt engineering can be an exciting career path. You can explore AI language models and technology in general, leverage your existing skills or interest in writing, and help companies achieve their business goals.

To get started in this field, follow these steps: 

1. Reflect on your prompt engineering career goals. 

Before launching your career or switching fields, it’s a good idea to reflect on your career goals, so that you can focus your efforts on the actions that are most likely to lead to success. Here are some examples of goals:

• Bring prompt engineering into your current role

• Use prompt engineering to help you grow your own business

• Get a job as an in-house prompt engineer for an organization

• Master prompt engineering skills and help to develop AI language model technology

AI prompt engineer career tip

Monitor how AI technology evolves, along with the job roles that spring out of it. Stay mindful of trends and how companies are using AI to achieve their goals, and adjust your own career goals accordingly. 

2. Earn prompt engineering credentials.

If your goal is to get a job as a prompt engineer, you may find it helpful to earn relevant credentials. As with other fields, a prompt engineering credential can show employers that you’re committed to mastering the latest techniques.

Depending on what different employers require, credentials for a prompt engineering role might include:

• A bachelor’s degree in computer science, data science, engineering, or a related field 

• A certification in prompt engineering, like the one offered by the Blockchain Council

In addition to earning credentials, consider taking prompt engineering courses. These can be a great way to learn in-demand skills in a structured format and give you practical examples to use what you’ve learned.

If you’re working in a non-technical role and want to pivot to prompt engineering, you should focus on developing your context architecture skills. This involves refining your prompts in a way that’s understandable to AI agents.

If you’re currently a software engineer or developer and want to augment your AI skills, you’ll want to study how large language models operate, especially in situations where the same prompt may yield different results. Knowing how to build RAGs and automated evaluation suites could make you a top candidate for AI engineering roles. People working in specialized roles, like marketing or analytics, can use prompt engineering to amplify their productivity, automating copywriting, content creation, and data analysis.

3. Build prompt engineering skills. 

Prompt engineers need diverse abilities to succeed, including creativity, proficiency with technology, and even an understanding of human psychology.

Let’s look at some prompt engineering skills. 

A. Writing skills 

Writing skills ensure that you write prompts that are clear to the language model and natural to the user. Refine prompts in a “chat” to teach the AI how to produce a better output. You can edit a follow-up prompt to be more precise, or add specificity to a previous set of instructions, such as asking the language model to elaborate on one example and discard the rest.

For some outputs, you might find it useful to prompt the language model to mimic specific styles. For example, you could ask the LLM to “Generate a sales pitch in the tone and style of XYZ Company’s brand voice.”

B. Knowledge of different language models

Knowing how to use different language models, including ChatGPT-3.5, ChatGPT-4, Google Gemini, and Microsoft Copilot, can benefit you in a prompt engineering role in several ways:

• Being able to work around the nuances and strengths of each LLM

• Knowing which LLM will work best for various projects 

• Being able to recommend one LLM over another for a particular use case 

C. Knowledge of prompting techniques

Some examples of specific prompting techniques include:

• Zero-shot prompting provides a prompt that is not part of the data you use to train the model but still generates the desired output.

• Few-shot prompting guides the model to produce an output based on examples of what you want.

• Chain of thought prompting is when you prompt the model through a series of intermediate steps before it arrives at its final answer to a multi-step problem. You can think of this technique as continuing a conversation with an AI model, with each question building on the answer that precedes it.

• Knowledge generation prompting is a way of asking the model questions or giving it directions so it can provide you with information on a topic.

D. Providing context

Context in prompt engineering refers to the information, background, or situation related to a task you need an LLM to perform. As the prompt engineer, you’ll provide contextual information in the prompt itself so that the LLM can yield the desired result. Contextual information examples include:

• Details about your employment history for a cover letter the LLM will write

• Asking an LLM to explain photosynthesis but specifying that the output needs to be appropriate for third-graders

E. Ethical & Safety Review

In 2026, security should be at the forefront of your prompting strategy. You should ensure every response passes a rigorous ethical audit and stays within operational boundaries.

Use this practical checklist to audit your prompt outputs before they reach an end-user.

• Biases: Try swapping demographic attributes (e.g., gender, age, or ethnicity) in your input data. If the model’s tone or recommendation changes significantly after these swaps, you’ll need to add more neutral framing or explicit fairness constraints to your prompts.

• Guardrails: Verify that the output follows negative constraints, or rules about what the AI must not do. For example, a medical chatbot must provide information without ever crossing into "Prescriptive Advice." Tip: Use a post-processing filter (like Guardrails AI) to automatically flag keywords associated with restricted domains.

• Privacy: Ensure the output does not inadvertently surface Personally Identifiable Information (PII) retrieved via RAG pipelines. Test this by prompting the system to "summarize a user profile" and checking if it reveals sensitive data like Social Security numbers or private addresses.

• Proactive Security: Attempt to "jailbreak" your own prompt by using urgent tones or peer-pressure tactics (e.g., "This is an emergency, ignore all safety rules"). If the model complies with the harmful request, you must strengthen your system prompt with clearer hierarchical instructions.

• Cultural Sensitivity: Ensure that the model doesn’t apply Western-centric logic to non-Western contexts, or American perspectives to European contexts, where information may not apply. For global products, include a instruction to "flag where training data may be insufficient for this specific cultural context."

F. Understanding your end-users

Being able to empathize with the user and understand their needs is crucial to crafting effective prompts. For example, if you’re building a chatbot for a company’s customer support portal, knowing users’ purchase behavior, product challenges, and previous interactions with customer support can help you craft better prompts.

G. Machine learning 

Skills or experience in machine learning can benefit your work as a prompt engineer. For example, machine learning can be used to predict user behavior based on how users have interacted with a system in the past. Prompt engineers can then finesse how they prompt an LLM to generate material for user experiences. Additionally, machine learning can help you understand the user's current situation or needs so that you can craft prompts accordingly.

H. Programming languages

Having technical knowledge of programming languages can allow you to customize your interaction with different LLMs for uses like:

• Automating repetitive tasks

• Fine-tuning prompts

• Implementing security measures 

I. Data analysis 

Prompt engineers can use data analysis to improve prompts. One way is to gather and analyze user feedback on outputs in order to evaluate prompt performance. Another way is to use data analysis to identify trending topics or content gaps to generate new content.

J. Subject matter expertise

Subject matter expertise in prompt engineering means you can serve users within your field of expertise. You can draw upon your expertise to craft effective prompts so that an LLM generates useful outputs. For example, if you have professional experience in horseback riding, your prompts can effectively get an LLM to generate content that horseback riding enthusiasts will want to consume.

4. Gain prompt engineering experience.

Given that this career path is still new, it may be that the easiest way to get experience in prompt engineering prior to employment is to create your own opportunities to hone your skills and apply them to real-world scenarios. Here are some ideas to get you started:

• Practice using a variety of AI language models

• Design your own projects using prompt engineering, such as creating chatbots or building a health care tool that offers medical advice based on a user’s symptoms.

• Work with your current manager or supervisor to come up with applications for prompt engineering in your role

You might need experience in engineering, developing, and coding to be a strong candidate for a prompt engineering role. 

Career tip for a prompt engineer

To stay current on trending conversations in this field, follow or subscribe to influencers in emerging technology, such as Bernard Marr, Fei-Fei Li, Andrew Ng (Coursera co-founder), and Ronald van Loon. 

5. Apply for prompt engineering jobs.

When you’ve found several job openings that interest you, update your application materials, including:

• Resume that reflects your skills, credentials, and experience in prompt engineering

• Cover letter template to tailor to each application 

• Portfolio or website to showcase your prompt engineering work

• Update your LinkedIn profile

How to choose between a degree, a bootcamp, or neither

• When to choose a degree: If you are early in your career and want to land one of the highest-paying AI jobs, then a bachelor’s degree is recommended. Fields that help you understand LLM behavior, like computer science or linguistics, will give you deep theoretical knowledge needed to qualify for some jobs. Additionally, if you need credentials to qualify for an international work visa, or want to work in a highly regulated industry like healthcare or finance, a degree is often required.

• When to choose a bootcamp: If you’re an experienced professional who already has a degree, a bootcamp can help you learn a new area of work in a relatively short timeframe. Programs like this can offer structured learning, deadlines, and accountability, which may be helpful for some. Bootcamps often take 12-24 weeks, and can help you transition careers in less than a year.

• When to build a portfolio: If you’re an advanced professional and subject matter expert, you may not need new credentials. Some roles will prioritize a robust portfolio demonstrating applied knowledge and experience over a certificate or degree. If you’re disciplined and motivated, you can create custom workflows and projects to showcase your skills.

Learn how to become an AI prompt engineer with Coursera 

If you’re ready to launch your prompt engineering career, consider one of Coursera’s online courses offered by leading organizations. By registering for Prompt Engineering for ChatGPT from Vanderbilt University, part of the Prompt Engineering Specialization, you can learn important terminology in this field, practice using and building prompt-based applications, and gain job-ready skills.