Essential Prompt Engineering Skills

Key takeaways

• Prompt engineering skills include understanding artificial intelligence (AI) language models, being proficient in programming, effectively evaluating output quality, and identifying biases and limitations.

• You can improve prompt engineering skills by learning the basics about large language models (LLMs) and natural language processing (NLP), experimenting with different AI models, and learning Python.

• Programming skills are required for prompt engineering, focusing on Python, C++, Java, and R.

AI prompt engineering skills help you understand how a model behaves under a given set of circumstances. Begin your path toward your new career and discover essential prompt engineering skills and how you can excel in prompt engineering. If you’re ready to build your understanding of generative AI, consider enrolling in Vanderbilt University’s Prompt Engineering Specialization. In as little as four weeks, you’ll have the opportunity to practice hands-on skills to learn how to use LLMs to automate tasks and increase productivity.

What is prompt engineering?

Prompt engineering involves guiding a generative AI tool to produce helpful, relevant, accurate, and efficient results using as few prompts as necessary. Prompt engineers assist in improving an AI model by evaluating the quality of its output.

If you’re interested in becoming a prompt engineer, you can attain various skills to make the prompting process simple and reliable and potentially increase your odds of getting a job in this expanding field. Economists expect prompt engineering to grow by 32.8 percent from 2024 to 2030 [1].

Core competencies in prompt engineering

As a prompt engineer, you must understand how a particular model behaves under a given set of circumstances. Explore the essential nature of understanding how models function and a few other competencies central to this field.

Understanding model behavior

All models function differently. Larger models can perform complex tasks, and smaller ones can be more efficient for specific tasks. The quality of a model’s input training data matters: If you’re prompting your AI model to answer medical questions, you want it to be trained on medical documents for domain specificity, which refers to the terminology and context unique to a particular field of knowledge.

Different models have specific use cases where you can get the most effective performance. For instance, ChatGPT can be suitable for creative text generation, while BERT might be better for understanding context in search queries.

Many other models exist to choose from, each with its own specific use case specialization. Examples include:

• Bloom

• LaMDA

• DeepMind

• DALL-E 3

Crafting effective prompts

Effective prompts are clear and specific. Provide enough context and background information to get the output you’re looking for. You can also include specific instructions as part of your prompt.

Some additional ways to craft effective prompts include:

• Ask open-ended questions: Use sophisticated, evaluative prompts and yes-or-no questions to see what your model comes up with.

• Be specific: Does your AI model give answers as vague or as specific as the prompts you input? Does its output stay vague when your prompts get more specific?

• Enforce constraints: Limit the AI’s scope by enforcing a constraint on its word count, for example.

• Provide examples: Offering the AI examples helps it understand what you’re looking for.

• Try role-playing: Ask your AI to assume a specific theoretical position and explain a concept. For example, you could ask it to answer medical questions, like a physician speaking to first-year medical learners to get more direct answers.

• Work with prompt chaining: Prompt chaining is the process of using a sequence of miniature prompts to get an AI to answer a larger, more complex question. See how the tool performs while answering the questions to determine whether it can arrive at a logical answer.

Read more: ChatGPT Prompts for Learning: How to Write Effective Prompts to Learn More

Iterative refinement and optimization

You can fine-tune your prompt to get what you need from your AI model. Prompt engineers call this iterative refinement: It involves making subtle changes in wording, formats, and structures to get an accurate, relevant output.

Technical skills

As a prompt engineer, you optimize results, getting more from AI platforms by helping machines better understand human users' intent. Creating these queries is somewhat of an art form and requires diverse technical skills, including familiarity with AI language models and proficiency in coding.

AI prompt engineering skills: Familiarity with AI language models

Familiarize yourself with AI language models and relevant key concepts in prompt engineering. Examples include:

• Natural language processing (NLP): This technology is essential to an AI’s understanding of human language and generating human-like responses to queries.

• Machine learning (ML): This is a way to train an AI that allows you to create specific learning algorithms.

• Large language models (LLMs): These are language frameworks that utilize NLP and ML to create the “brain” of an AI. LLMs are key to modern generative AI.

Programmers use LLMs to train many of today’s AI interfaces, such as ChatGPT. These models utilize neural networks to parse enormous input data sets and ultimately provide the desired output. Transformer LLMs are capable of unsupervised training.

Language models have different use cases depending on the LLM programmers use to train them. When working with an AI interface, consider its parameters and specifications and how they might help you achieve your goal. Larger models contain more parameters. Examples of different LLMs and their parameters include:

• Open AI's GPT-3: 175 billion parameters [2]

• AI21 Labs’ Jurassic-1: 178 billion parameters with a vocabulary of 250,000-word parts [3, 4]

• Cohere’s Command: Can work in more than 100 languages [5]

Programming proficiency

Language models, including NLP models, commonly use Python, a programming language. As a prompt engineer, it’s important to be proficient with Python.

Reasons to learn Python include:

• It’s relatively easy: Python has a readable, clean syntax structure.

• It offers NLP libraries: NLP libraries make certain coding processes easier. Python works with NLP libraries such as Natural Language Toolkit (NLTK), spaCy, and Gensim.

• It’s cross-platform compatible: You can run Python on multiple platforms and reuse your code repeatedly.

• It’s easy to integrate: You can seamlessly thread Python into your workflow without additional tools or applications.

Other programming languages prompt engineers may need to know include:

• C++

• Java

• R

Analytical skills

Working smartly with AI means developing specific evaluative skills that AI lacks. Such uniquely human skills will help you iterate and refine your prompt strategy.

Evaluating output quality

When evaluating output quality, consider the model’s accuracy: What ratio of correct answers to incorrect answers did your model output? Consider its precision regarding domain specificity: Does your model have enough specialized knowledge to accurately address field-specific prompts?

To evaluate your model’s output quality, you might use techniques such as:

• A/B testing: This involves two users utilizing two different AI models and seeing which model responds better to a prompt.

• Continuous monitoring: Ensure your AI model continues to perform well in the face of changing data and shifts in requirements or preferences.

• The human touch: LLMs can only do so much. It’s up to you to ensure the output is accurate and relevant and reads fluently. You need to be able to explain the relationship between your prompt and its production to all stakeholders, regardless of their level of technical knowledge, so communication skills are essential.

Identifying biases and limitations

Bias can be a risk while working with LLMs because they’re trained on large sets of unstructured data containing different content and viewpoints.

Bias can have real-world consequences. This includes harmful stereotyping, such as gender bias and biases against certain ethnicities and sexual minorities. These biases could result in outcomes like poor patient care when physicians rely on biased AI to help treat patients.

Is it hard to become a prompt engineer?

Prompt engineering is an achievable field to enter after you develop a foundational knowledge of large language models, machine learning, and natural language processing. While it certainly requires some technical skills, prompt engineering also requires a broader skill set, with skills such as communication and problem-solving also being important. As a result, aspiring prompt engineers don’t necessarily have to come from a technical background, making it a realistic career choice for people from various backgrounds, including liberal arts.

How to improve prompt engineering skills

From learning the fundamentals to sharpening your proficiency in working with specific coding languages, gaining competencies in prompt engineering is multi-fold. You can gain prompt engineering skills by:

• Learning the basics: Learn key concepts such as LLMs, NLP, and ML to gain prompt engineering skills.

• Burnishing your language skills: Make sure you communicate effectively without relying on technical jargon.

• Experimenting: Interface with different AI models and see how prompting goes.

• Learning Python: Python is key in today’s AI world. You can learn it via video tutorials, books, boot camps, or courses.

Explore free AI resources

Subscribe to Career Chat on LinkedIn, where you can stay up-to-date on emerging trends. Then, you can check out these free resources to learn more about AI:

• Bookmark this page: Artificial Intelligence Glossary: Learn AI Vocabulary

• Read our Career Chat issue: 3 GenAI Prompting Methods Worth Trying

• Learn from an expert: The AI Advantage: 9 Questions with UC Davis AI Instructor Sadie St. Lawrence

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