When AI first became part of our daily lives, it was introduced as the solution to many things, one of which was writing: a silver bullet to all your writing woes. From creating contracts to crafting corporate emails, suddenly, we had this tool that was able to do it all for us. Not only that, but it could do it well and it could do it pretty much instantly.
Of course, while AI (whether it’s Chat, Perplexity, Claude, Gemini or any other model) is undeniably incredible, especially when it comes to writing, we now know that it’s still fallible. It makes mistakes, it hallucinates and it’s simply not completely reliable. Not only that, it lacks a certain “human touch” that while we can’t always explain exactly what is, we just know it’s missing.
But, much like most things in life, there’s a limit to what AI can do. One of these constraints is the fact that AI is only as useful as the instructions you give it. And that’s where prompt engineering comes in.
The term has become increasingly common as generative AI has moved from being a novelty to a serious business tool. While we’re mostly concerned about the jobs AI is taking and how many humans will be replaced by AI systems, this may be a situation in which AI is actually creating a job.
What Is Prompt Engineering?
Put simply, prompt engineering is the process of designing, testing and refining instructions given to an AI system to get a particular result. A prompt is just the input you give an AI model: it could be a question, an instruction, a description or a combination of all three.
Prompt engineering is about working out how to phrase that input so the AI understands what you actually want. Similar to how when you Google something, typing in an entire sentence with perfect grammar isn’t going to be as effective as using keywords.
According to IBM, effective prompt engineering helps generative AI systems produce outputs that are more relevant, accurate and aligned with the user’s intended goal.
That might sound obvious. After all, if you ask ChatGPT to “write something about startups”, you’re probably going to get something fairly vague. But, if you tell it that you’re writing a 700-word article for a technology publication aimed at startup founders, ask for a conversational tone and specify exactly which aspects of the startup ecosystem you want covered, you’ve given the model considerably more to work with and it’s more likely to produce something a little closer to what you’re looking for.
Why Does Prompt Engineering Matter?
Generative AI doesn’t necessarily know what you mean simply because you know what you mean, and that’s something that many people don’t quite seem to understand (and this was a problem even before AI shifted into the mainstream). Humans regularly leave things unsaid because we rely on context, so AI models are much more likely to produce useful results when you provide that context explicitly.
A good prompt can tell an AI model what to do, who the output is for, what information it should use, what format the answer should take and what it should avoid.
For instance, in a professional situation, an employee asking an AI tool to “summarise this customer feedback” might get a reasonable answer. But, a carefully constructed prompt could instead ask the system to identify the five most common complaints, group similar issues together, quote representative examples and present the findings in a format suitable for a management report.
In this case, the underlying AI hasn’t necessarily changed, but the instructions have, thus producing a far better result.
How Does Prompt Engineering Work?
Unfortunately, as convenient as it would be, there isn’t one magical formula for writing the perfect prompt. In fact, much of prompt engineering involves testing, reviewing and refining.
One of the simplest approaches is to be clear and specific. Give the AI enough context to understand the task, explain what you want the output to look like and provide examples where useful.
This can also involve techniques such as zero-shot and few-shot prompting. Zero-shot prompting means asking an AI model to complete a task without giving it examples of what you want. Few-shot prompting, meanwhile, gives the model a small number of examples to demonstrate the desired result.
For example, rather than simply asking an AI to classify customer reviews as positive or negative, you could provide several examples showing exactly how you want different types of reviews categorised. This means that it’s not only using its own judgement (which is, of course, based on information and date it’s been trained on), but it can actually start to interpret your own judgement too, and if it’s effective, replicate that.
Is Prompt Engineering Just Writing Good Questions?
It’s not just about asking good questions, although that’s a big part of it. Prompt engineering can range from simply improving the wording of a request to much more sophisticated processes involving prompt templates, examples, external data, testing and automated workflows.
PromptEngineering.org describes the discipline as involving the systematic design and optimisation of prompts, rather than simply coming up with clever sentences. And, this is where prompt engineering starts becoming particularly relevant to startups and businesses.
A company building an AI-powered customer service tool, for example, isn’t going to manually type a beautifully crafted prompt every time a customer asks a question, because that’s simply not realistic. It’s difficult and it’s time consuming. Developers, on the other hand, can create structured prompts and workflows that consistently tell the underlying model how to behave. Thus, in this kind of situation, the prompt almost becomes part of the product.
Do You Need To Be A Prompt Engineer?
Not everybody needs to be a prompt engineer per se, but if you’re going to be using AI consistently, especially in a professional situation, it would be a good idea to try and understand it as far as possible.
The good news is that in many everyday situations, the basics are surprisingly simple. You must be specific, provide context, explain the desired outcome and don’t be afraid to refine your request.
But, professional prompt engineering requires a lot more technical knowledge than just being clear. At the highest level, it can require engineers to also understand large language models, programming, data structures, algorithms and the limitations of AI systems. So, there’s is a difference between being good at prompting an AI tool and working professionally in prompt engineering.
How Long Will Prompt Engineering Be Relevant?
This is the question we ask across the board in technology and AI these days, as innovation as constant and technical skills seem to become irrelevant quicker than they appear. Indeed, as AI models become better at understanding natural language, the need to obsess over exactly which words is most likely going to decrease.
But the thing is, that doesn’t mean the underlying concept of prompt engineering is disappearing altogether. It’s probably just going to change and evolve. Businesses still need to decide what they want AI systems to do, what information they should have access to, what rules they should follow and what a successful result looks like.
So, prompt engineering may become less about simply finding the perfect sentence and more about designing effective interactions between humans, data and AI systems. That is, it’ll probably become more advanced and complex. But hey, these days, what isn’t?
