With the bombardic rise of generative AI in the last year, prompt engineering has become an exciting new career exploding along. Here is an intro to what it is and the top 10 skills required to become prompt engineer.
Generative AI exploded almost a year ago with the launch of ChatGPT in 2022, and has snowballed into something even more massive. While people were still trying to understand artificial intelligence, the term gen AI took the world by storm. With its easy-to-use and available-to-all features, it became a household name.
More than that, companies tapped into its benefits left and right, using this still-changing technology in different aspects of their working. With the plethora of Gen AI tools such as ChatGPT, Midjourney, Bard, DALL-E, etc. the world has begun moving towards a highly tech-driven approach. All these tools work on one thing- prompts.
But have you ever wondered what would happen if you aced the art and science behind writing a prompt? What exactly makes one prompt different from another one? Who are these people who can get these tools to perform and give an output as per their exact needs? If you are interested in finding an answer to these questions, then you are just at the right place!
Generative artificial intelligence has given birth to dozens of job roles and opportunities and one such gig is that of a prompt engineer. Prompt engineering and gen AI go hand-in-hand. The former will continue to expand as long as the latter keeps inventing itself.
So, here in this blog, we cover everything from what is prompt engineering to what these professionals do and the skills to become one. Towards the end, you will also get a brief about the future of this field to help you better decide whether to take the path or not. Without any further ado, let's get started!
In simple terms, prompt engineering refers to the process of guiding generative artificial intelligence solutions/models to generate desired outputs. We all know that Gen AI tries to mimic humans. However, that does not happen on its own. It needs to be given clear instructions to derive relevant and high-quality outputs. The art and science that goes into writing these prompts is referred to as prompt engineering.
For example- Let's say you've decided on making pasta for dinner. Now, the sauce from the jar is fine. However, wouldn't it taste better if you went to the market, picked fresh tomatoes, and made the sauce yourself? Or even better, what if you grew the tomatoes in your own garden and plucked them fresh? You'll unlock a whole new level of deliciousness.
This is exactly how prompt engineering works! The better the input (prompt), the better the output (answer) will be. The skill around crafting the right prompt every time and twisting it as per the requirements is known as prompt engineering AI.
Large language models are human level prompt engineers. These use NLP for understanding and generating human-like text. Let's see what a prompt engineer AI does to better understand what your learning journey should look like.
The role of a prompt engineer revolves around working with cross-functional teams and discussing product development. You must identify the use of various AI tools to design, develop, and consequently refine prompts for different applications.
Some of the most commonly carried out tasks by these professionals include -
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Becoming a pro at ChatGPT prompt engineering is not something you can just learn overnight. The way to acing this profile is long and it is essential to garner skills every chance you get. It is something you must do in steps and learn on the go. Taking every challenge as a learning opportunity should be your key motto to grow.
Here is a list of the top 10 skills required to become a successful and desirable prompt engineer.
Knowing the underlying principles of different AI models is important, especially how they are trained, evaluated, and structured. This incorporates convolution neural networks (CNNs), transformer-based architectures, and recurrent neural networks (RNNs).
In-depth understanding of Machine Learning models and fundamentals is needed to train and fine-tune AI models. This consists of techniques such as clustering, dimensionality reduction, regression, and classification. Knowledge of unsupervised and supervised learning algorithms is also imperative.
Having an in-depth understanding and knowledge of NLP techniques as well as libraries will help you gain high working efficiency with text-based AI models. You will benefit with knowledge of concepts such as sentiment analysis, tokenization, topic modeling, and named entity recognition.
AI engineering commonly revolves around working with humongous datasets. Hence, you must also have skills in data cleaning, feature engineering, data augmentation, and data pre-processing. This will ensure high data quality and superb model performance.
Python is one of the most widely employed programming languages in AI development. Hence, having proficiency in this language is a must. You can also learn other often used programming languages. In addition to this, understanding of algorithms, object-oriented programming concepts, and data structures will help.
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There are many prompting techniques that have gained traction to derive the best outcomes, and you should be aware of them all. Some of the most widely accepted and used prompting techniques are zero-shot prompting, few-shot prompting, chain of thought prompting, and knowledge generation prompting.
OpenAI prompt engineering for ChatGPT can bring along many problems, and not only this model but a lot others too. It is not guaranteed that you will get your desired output. In fact, sometimes you might not be able to think of the right prompt to trigger a useful output. Good problem solving skills are essential.
Have you ever thought what you would get if you fed 'rewrite the preamble of the US constitution in pirate style' on ChatGPT? Well, the result is certainly much funnier than anything you can ever imagine.
This is domain expertise, which can be used in open AI prompt engineering, or even on other models like Midjournery. Craft anything you wish to by putting int exactly what you want and how you want it.
Be prepared for a long walk to excellence. And even then, keep the door open for more learning. This is a field that has no stopping point, at least we don't see one yet. However, that is also a point for the pro side of the list. Since no end has been found to this field yet, you have an endless sea to sail.
We know that LLMs are not sentient. However, they do have the ability to communicate in a manner similar to humans. The way the LLM responds depends heavily on how you begin conversing with it and how well you are able to showcase your needs. Learn different conversational approaches and gambits to switch your style as per the needs.
One skill that you require for certain is patience. And yes, it can be learned and polished with time too. No language model is as smart as humans. Thus, it might not get what you are trying to convey, requiring you to slow down and practice patience. Keep trying different prompt engineering examples to understand the best ones.
We are standing at the cusp of an AI-driven era, wherein prompt engineering has emerged in a pivotal role. While the field is still in a nascent stage, the future is expected to be bright.
The AI space is highly dynamic as more research and studies take place. Consequently, more innovations stem forth and that too at a rapid pace. Here are a few of them-
Like any field that is still in its developing phase, it comes with a set of challenges too. These include -
Prompt engineering is here to stay as it has embedded itself in both personal and professional circumstances. Today, AI models are more complex than they used to be a couple of years ago. Similarly, in the coming years, the complexity would grow and their use cases would multiply too.
Prompt engineers would be at the center of it all, ensuring models remain user-friendly, contextually relevant, and accessible.
The space that AI has created for itself is vast, knit intricately, and also continuously evolving. Hence, nothing is permanent, not even the demand one might think of today. Amid all this, the demand for those engineers with prowess in prompting has risen. It all lies in the abilities to induce the desired answers by crafting the correct input.
As per McKinsey's report titled State of AI in 2023, plenty of organizations are using gen AI tools. Additionally, over 40 percent businesses are expected to increase their investments in gen AI technologies, leading to a direct positive effect on the demand for prompt engineers.
While it is a relatively new discipline, it is here to stay as it holds the key to unlocking the true hidden potential of AI models. Since AI seems to have so effortlessly blended into our lives, this field has emerged to be one of the most powerful ones today.
AI prompting is a skill that is gradually becoming indispensable for organizations everywhere. It is much more than asking questions. It is about strategically crafting queries to get the AI systems to respond effectively.
Prompt engineering lies somewhere between tough and easy, depending upon what approach you take. It involves learning in steps to build expertise over a period of time. Also, taking a course and earning certifications helps.
While you do require an understanding of AI frameworks and programming languages, you do not have to be a pro coder.
If you have a relevant degree like engineering or computer science, then it will certainly be helpful. However, you could be from any background and still become a prompt engineer by taking help of industry experts and getting certified.
The average salary of a prompt engineer in the US is around $63k per annum.
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