Skills to Acquire as an AI Prompt Engineer

AI Prompt Engineering: Top Skills to Acquire as an AI Prompt Engineer

AI is here.

And it can do everything!

All you have to do is sit back, relax, and let your handy AI assistant read your thoughts and perfectly execute whatever work you have in mind for it…

Obviously, we’re joking.

AI isn’t quite there yet.

It’s impressively advanced, and capable of handling a wide range of tasks – but it still needs human guidance, support, and supervision to be effective.

That’s where prompt engineers come into play. Prompt engineering, the process of crafting prompts and instructions to facilitate better AI results, is indispensable for harnessing the full power of any generative AI tool.

So what skills do you need to develop as a prompt engineer in your own right?

What Is an AI Prompt Engineer?

With respect to AI, prompt engineering is the strategy of deliberately structuring and phrasing prompts for generative AI engines so that the results are most closely aligned with the user’s needs and intentions. Carefully choosing your words, structuring your prompt appropriately, understanding and contemplating the strengths and limitations of the AI you’re using, and iterating and refining your prompts can all play a role in helping you succeed.

Prompt engineers are professionals who specialize in prompt engineering, and utilize the greatest strengths of AI to achieve their ends. This could mean using AI to generate content, produce scripts and snippets of code, or even conduct research.

Why Does Prompt Engineering Matter?

Why do we care about prompt engineering?

The short version is that modern generative AI, while impressive, is associated with some limitations. While most generative AI engines are designed to be extremely usable and intuitive to the average user, using AI like a basic search engine or creating prompts carelessly could result in problematic outputs.

Practicing better prompt engineering means:

  •       Tapping into the full potential of generative AI. At their best, AI systems can produce world-class content, help solve difficult problems, and facilitate greater productivity. But if your prompts aren’t on point, you may only end up seeing 70 or 80 percent of that full potential. Thoughtfully creating a better prompt can instantly elevate the relevance, trustworthiness, and overall quality of your results.
  •       Preventing hallucinations. One of the biggest problems with the current generation of generative AI tools is AI hallucinations, wherein AI engines make reference to facts that aren’t true, events that don’t exist, or nonsensical phrases that don’t belong. Skilled, experienced prompt engineers understand that AI hallucinations exist, they recognize how those hallucinations are formed, and they can definitely manipulate their prompts to avoid them.

generative AI

  •       Curating better results. Generative AI has incredible potential, but results aren’t always aligned with user intentions. If you ask for a generically written article, it could end up too long, too short, too wordy, poorly organized, or misaligned with your original vision. Prompt engineering is a way to curate much better, goal-oriented results.
  •       Saving time. Given enough time to experiment, even someone with no prompt engineering knowledge or experience can eventually manipulate AI engines into producing what they want. Enough rounds of “no, not that, please try again” can lead you down a winding road that eventually reaches your intended destination. The problem with this chaotic approach is that it’s incredibly time-consuming. If you practice effective prompt engineering from the beginning, you can take a process that would normally take 12 steps and reduce it down to 1 or 2.

Skills to Acquire as an AI Prompt Engineer

So what skills do you need to acquire as an AI prompt engineer?

  •       Linguistic mastery. First, you need linguistic mastery. The most powerful generative AI tools we have available rest on a dependency for parsable human language. Thanks to advanced natural language processing (NLP), these tools can interpret almost any intelligible phrase, but to see better results, you need to be able to choose specific, concise, accurate words – and be aware of potential ambiguities and limitations. The phrase “write a paragraph on the topic of wheat grass nutrition” is simple enough for an AI to understand, but any linguist can tell you that there are notable ambiguities here; what is the purpose of this paragraph? How long should the paragraph be? What elements of nutrition should be taken into consideration? Are there multiple types of wheat grass to consider? Should it be compared to other, similar nutritive substances? The list goes on and on.
  •       Critical thinking. Prompt engineers also need to be able to think critically. AI is notoriously opaque. When it gives you a bad answer, or an insufficient one, it’s not always obvious why or how this response was selected. Prompt engineers are most successful when they’re able to assess the situation and diagnose the problem. Critical thinking also allows you to speculate alternative phrases and iterative phrases that might build upon your initial results, giving you a noticeable boost in intellectual agility to better navigate this complex territory.

prompt engineer

  •       Lateral thinking and creative problem solving. Similarly, the past to good AI output isn’t always straightforward. AI prompt engineers need to develop lateral thinking and creative problem solving, which are intricately interrelated. Lateral thinking forces you to deviate from standard, traditional, or familiar paths of logic to brainstorm new approaches, while creative problem-solving forces you to speculate abstractly and without form to see problems from new angles. Both are indispensable as you improve your skills and learn to work with many different types of AI.
  •       AI and NLP familiarity. It also helps to have some familiarity with how AI and NLP work. When you understand the algorithmic processes that generative AI engines apply to prompts, you can anticipate potential problems with the prompts you create and proactively resolve them. Additionally, as you gain more experience through trial and error, you’ll be able to “get inside the head” of the machines you’re working with and cater to their unique styles of data processing. This is also useful if you plan on building or customizing your own AI tool.
  •       Basic scripting and programming. It also helps to be familiar with some of the basics of scripting and programming. If you plan on creating an AI tool of your own, there’s some obvious utility here. But learning scripting and programming is also a great way to train yourself to think logically, objectively, and algorithmically. Scripting and programming require focused precision, with little room for error, and in some ways, prompt engineering is similar.
  •       Ethical awareness. Many companies adopting generative AI tools are acutely aware of risks related to ethics. That’s why good prompt engineers should also develop and refine their own ethical awareness. You should be at least passingly familiar with some of the biggest ethical complexities associated with AI, including the need for transparency, potential for bias, and inter-relational dynamics between humans and AI. You should feel confident in engineering prompts that can help you avoid some of the biggest ethical issues associated with AI, while reducing risk for whatever organizations you work for.
  •       Tone and style in writing. One of the selling points of generative AI is that it can replicate tone and style, assuming you give it a proper input. Accordingly, prompt engineers should be familiar with the basics of creating effective tone and style in writing. This serves many purposes, allowing you to identify tone and style in AI output so you can judge its quality, while simultaneously giving you tools you can use to get the right tone and style for the right piece of content. On top of that, prompt engineering is often best practiced with a flat, objective, straightforward style that AI can easily interpret. Writing for a machine effectively is much different from writing for human beings.
  •       Domain expertise. Prompt engineers should be masters of their domain, whatever that happens to be. If you want to use AI to generate code for specific apps, you should be familiar with that style of coding. If you want to write an article, you should be an expert writer. If you want to use it to research legal cases, you should be familiar with the law in whatever area you’re researching. This is going to help you catch hallucinations and mistakes, and should also help you point the AI in the right direction for whatever problem you’re trying to solve. Remember, AI isn’t here to replace experts; it’s here to supplement them and support them.
  •       AB testing. AB testing is a practice heavily used in marketing and advertising, in which two variations of a given message are produced and tested against each other in a dynamic experiment. By the end of that experiment, it should be obvious which version is superior; one will likely generate more traffic or conversions than the other. Skilled prompt engineers should be familiar with the basics of this process, and they should readily deploy multiple versions of developed prompts to see which types of prompts are most effective in a given environment.
  •       Iterative learning and development. Prompt engineers don’t have the luxury of being able to rest on their laurels. That’s because the world of AI is rapidly advancing, with new technologies emerging on a nearly daily basis. A prompt that worked a certain way a few weeks ago may produce very different results because of a new update. Similarly, as we collectively learn more about generative AI and prompt engineering, we unlock new potential within ourselves. For these reasons and others, it pays for prompt engineers to practice iterative learning and AI development, constantly advancing their own abilities.
  •       Root cause analysis. Root cause analysis is the process of identifying the figurative “first domino” in chained sequences that lead up to a specific problem. In the context of prompt engineering and AI, this usually means identifying a problematic output from your AI engine, then reverse engineering why and how that output emerged. Do this consistently enough, and you’ll eventually learn the weaknesses and limitations of your AI system so you can better avoid them in the future – and you’ll reveal holes in your own approach to prompt engineering that you can iron out with practice.
  •       A sense of humor. Finally, while it’s not a skill you can put on your resume, it pays to develop a sense of humor as a prompt engineer. AI is a bit funny, especially when it goes off the rails with a particularly strange hallucination. You need to be prepared for some of these pitfalls and moments of awkwardness if you want to eventually succeed in your career. AI is a serious topic – but we don’t always need to take it so seriously.

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Chief Revenue Officer at Software Development Company
Timothy Carter is the Chief Revenue Officer. Tim leads all revenue-generation activities for marketing and software development activities. He has helped to scale sales teams with the right mix of hustle and finesse. Based in Seattle, Washington, Tim enjoys spending time in Hawaii with family and playing disc golf.
Timothy Carter