What Is Prompt Engineering? And How to Write Effective Prompts

Points clés à retenir

Prompt engineering is the act of outlining a specific task for a generative AI tool to complete

• Prompt engineering is the process of making and refining requests to improve the accuracy, specificity, and usefulness of a GenAI tool’s outputs

• It involves using various techniques such as zero-shot, few-shot, and chain-of-thought prompting to guide large language models (LLMs) toward a desired result

• Whether you’re prompting ChatGPT to help you strengthen your resume or using DALL-E to generate a photo for a presentation, anybody can be a prompt engineer. 

You can consider enrolling in the Vanderbilt University Prompt Engineering Specialization. You’ll learn how to tap into the emerging capabilities of large language models to automate tasks, increase productivity, and augment human intelligence

Best Prompt Engineering courses for beginners

Top-rated beginner-friendly Prompt Engineering courses with no prerequisites

1. Generative AI: Prompt Engineering BasicsIBMBeginner1 – 4 WeeksCourse
2. Prompt Engineering for ChatGPTVanderbilt UniversityBeginner1 – 3 MonthsCourse
3. Google Prompting EssentialsGoogleBeginner1 – 4 WeeksSpecialization

What is prompt engineering?

Generative AI can create text, images, video, and more. Prompt engineering is the process of refining what you ask a generative AI tool to do. Anyone can do this using natural language in generators like ChatGPT or DALL-E. It’s also a technique AI engineers use when refining large language models (LLMs) with specific or recommended prompts. 

For example, if you’re using ChatGPT to brainstorm a professional summary while refining your resume, you might begin by prompting it with a simple command like, “Write a sample professional summary for a marketing analyst.” Since that prompt is on the vague side, you might follow up with additional context or feedback: “that was too formal” or “shorten to less than 100 words.”

Learn more:How to Become a Prompt Engineer

Why is prompt engineering important?

Prompt engineering is important for AI engineers to create better services, such as chatbots that can handle complex tasks like providing customer service or generating legal contracts. Making sure that generative AI services like ChatGPT are able to deliver outputs requires engineers to build code and train the AI on extensive and accurate data

We’ve reached a point in our data-driven world where training AI models can help deliver solutions much more efficiently without manually sorting through large amounts of data. Proper prompt engineering can also identify and mitigate prompt injection attacks (malicious attempts to hack the logic behind ChatGPT or chatbots) to ensure companies deliver consistent and accurate services

Learn to give AI clear instructions so it acts as a professional collaborator with the Google AI Professional Certificate

What does prompt engineering look like?

Here are a few examples of prompt engineering to give you a better understanding of what it is and how you might engineer a prompt with a text and image model:

For text models like ChatGPT:

• What’s the difference between a professional summary and an executive summary?

• Write a professional summary for a marketing analyst looking for a marketing manager job

• Now trim it down to less than 60 words

• Rewrite it with a less formal tone

For image models like DALL-E: 

• A painting of a cat

• A painting of a cat chasing a mouse in Impressionist style

• Now use only warm tones in the painting

What does a prompt engineer do?

As a prompt engineer, you will design and test prompts for AI applications to get accurate responses based on the purpose of the app, such as a customer service chatbot or an AI-enhanced software product. You can work in a variety of industries, including health care, cybersecurity, business, and education. Because the field is new and growing rapidly, you’ll keep up with the latest advancements in AI and apply them to continuously improve your processes.

How to engineer generative AI prompts

Here’s a quick step-by-step guide to engineering your prompts

1. Express your query as clearly as possible

Because generative AI is a deep learning model trained on data produced by humans and machines, it doesn’t have the capability to sift through what you’re communicating to understand what you’re en faitsaying. 

What you say is what you get

When inputting a query into an AI generator, it’s best to query with specific, plain language and context. For example, instead of, “Write an outline that includes a title and next steps,” you could query, “Write an outline for an academic research proposal that includes sections for title, summary, and next steps.” 

2. Experiment to come up with best practices

For each type of output, such as a brief outline, research proposal, or resume bullet points, you’ll want to experiment with the generative AI by using different variations of the same request. This way, you’ll determine whether you need to include guidance such as “in a formal tone of voice.” If you do need to include “tone” in your prompt, should you write “in a professional tone” or “in a formal tone”? 

Play with your inputs, too. Consider inputting sample outlines in a prompt or providing examples you’d like the generator to model

3. Follow up with instructions or questions

Once you’ve shaped your output into the right format and tone, you might want to limit the number of words or characters. Or, you might want to create two separate versions of the outline, one for internal purposes

The generator can do this based on the output it provided earlier. Iteration is your friend. Continue to “engineer” the prompt until you achieve your desired results

4. Play with different prompting techniques

Generative AI is still a new (and developing) technology, but that doesn’t mean researchers haven’t already developed some strategies for designing effective prompts. As you play with generative AI, try using some of these prompting techniques to achieve the results you’re looking for:

• Zero-shot prompting:This is the most direct and simplest method of prompt engineering in which a generative AI is simply given direct instruction or asked a question without being provided additional information. This is best used for relatively simple tasks rather than complex ones

• Few-shot prompting:This method involves supplying the generative AI with some examples to help guide its output. This method is more suitable for complex tasks than zero-shot prompting

• Chain-of-thought (CoT) prompting: This method helps improve an LLM’s output by breaking down complex reasoning into intermediate steps, which can help the model produce more accurate results

• Prompt chaining: The prompter splits a complex task into smaller (and easier) subtasks, then uses the generative AI’s outputs to accomplish the overarching task. This method can improve reliability and consistency for some of the most complicated tasks

These are just some of the prompting techniques that you might play with as you continue to explore prompt engineering. Often, in fact, the most effective prompt strategy is to combine several different techniques to achieve the desired output

The future of prompt engineering

Prompt engineering will continue to evolve in this era of AI and machine learning. Soon, there will be prompts that allow us to combine text, code, and images all in one. Engineers and researchers are also generating adaptive prompts that adjust according to the context. Of course, as AI ethics evolve, there will likely be prompts that ensure fairness and transparency

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Best free Prompt Engineering courses

High-quality free Prompt Engineering courses you can start today

1. Essentials of Prompt EngineeringAmazon Web ServicesMixed1 – 4 WeeksCourse
2. ChatGPT Prompt Engineering for DevelopersDeepLearning.AIBeginnerLess Than 2 HoursProject
3. Large Multimodal Model Prompting with GeminiDeepLearning.AIBeginnerLess Than 2 HoursProject

Best Prompt Engineering professional programs

Earn a certificate in Prompt Engineering from top universities and companies

1. Prompt EngineeringVanderbilt UniversityBeginner1 – 3 MonthsSpecialization
2. IBM Generative AI EngineeringIBMBeginner3 – 6 MonthsProfessional Certificate
3. Generative AI FundamentalsIBMBeginner3 – 6 MonthsSpecialization

Updated on Mar 19, 2026
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