Choosing a career path can be overwhelming, given the many options and the uncertainties involved. In this article, I propose a scientific approach to career guidance, employing data analysis to streamline the decision-making process.

This innovation aims to significantly enhance career exploration and professional awareness by using a tool designed to leverage advanced data analysis and neural networks, potentially benefiting the broader economy. The solution harnesses the vast potential of data to uncover hidden patterns and correlations, offering personalized career recommendations that align with an individual’s unique characteristics.

In essence, this data-driven approach acts as a compass in the complex world of career planning, pointing individuals towards paths that resonate with their personal and professional selves. My mission is to provide a solution that reduces confusion and complexity, ultimately streamlining the process of identifying suitable career paths for future generations.

Underqualified. Overqualified. Underemployed.

From a young age, we have thought about our future career. Perhaps there was a time when the appeal of a uniform caught our eye, simply because it seemed cool. But as time sped up and that crucial decision-making time edged closer, dreams took a wild turn: a model, an influencer, a gamer, a pilot, a lighthouse keeper, a hacker, or perhaps an actor?

Addressing this complex issue that affects everyone can be particularly challenging due to the multitude of variables involved. Each individual’s unique circumstances, perspectives, and needs add layers of complexity, making it difficult to find a one-size-fits-all solution. This complexity is compounded by the dynamic nature of societal, economic, and psychological factors that continuously shape and reshape the context of the problem.

Indeed, some schools have counseling programs, but how proficient are they? Chances are, finding the solution will require solo effort. Everyone aims for a job they’ll like, but defining ‘a likable job’ can be tricky.

We all want jobs that look good, pay well, and, in an ideal world, require minimal effort. To figure out the future career, we need to explore our interests, recognize the skills and traits, project the market demand, check finances, and assess how likely we are to succeed. It is a lot of work, and since people try to avoid hard work, they end up postponing the decision.

In some cases, being born into a family of doctors can make it seem like the career is already decided for you. This profession may feel natural, as the path appears well-defined and predetermined. However, not everyone’s options are so clear. Some parents try to show their kids as many options as possible, while others leave their kids to figure it out for themselves.

However, one thing many share is the pressure as high school ends. College applications are coming up, asking us to decide who we are going to be. It is time to make the big choice, one that will shape our future.

There are two options: go all-in now or “I’ll try to figure it out later.”

We might hope for relatives to figure it out somehow, but they’re waiting for us to step up and decide — it is our time to shine.

If we haven’t made up our mind, this mess usually ends with a “meh, let’s just pick any college we can pay for, close by, and somewhat “ok.” And that’s a feast for those not-so-good colleges that feed on uncertainty, are easy to get into, pay their teachers peanuts, and have a fireplace in the admissions office. And what’s the outcome?

In such cases, an investment over $100,000 may result in a potentially inapplicable & unemployable degree.

This is why numerous universities are introducing programs with titles that seem as though they were conceived at a last-minute brainstorming session, featuring names like Political Economy, Cybersecurity Leadership, Human Services, Managerial Economics, among others.

Spending a lifetime explaining to every potential partner, Uber passenger, employer, and psychiatrist the meaning of such title might not be the best option.

That’s a topic for a separate piece. For the moment, let’s agree that career path navigation may not be so straightforward. So, how do we solve this? It is time for some good old-fashioned science.

Mapping the Career DNA

What defines a perfect profession match? To understand this concept fully, let’s first establish some basic characteristics to know what we’re dealing with. Beginning with statement number one:

I. The profession needs to be individually perceived as dopaminergic (rewarding).

As neuroscientists explain, we’re all driven by dopamine, a neurotransmitter key to mood, motivation, and enjoying those ‘bright experiences’. Experiencing a sense of fulfillment/joy, specifically through seeing one’s input lead to tangible outcomes, is the basis for a sustainable career.

But isn’t competence important? It turns out, shockingly, knowing what you’re doing is essential. To perform the job, we need to have certain skills, collectively known as a skill set. A skill is the ability to perform tasks with proficiency. This brings us to our second revelation:

II. Individual’s skills have to match or exceed those required by the profession.

Skills come in a variety of colors and types, yet they share a common thread: skills are obtained through experience and knowledge. However, this doesn’t imply that anyone can be a professional opera singer. People vary in aptitudes; some excel in thinking, some thrive in obeying, while others are natural-born leaders. Why such a difference?

III. No skill is mastered without proper trait foundation.

Everyone has a different set of traits, and specific traits give rise to different skills. A trait is an inherent quality that provides an advantage in mastering particular skills.

If we were to visually illustrate this concept, it would look like this:

The proper foundation is everything. Mastering skills without the right prerequisites (traits) is a dead end; for example, you can’t excel in software engineering (the skill) without problem-solving (the trait). Similarly, you cannot be a successful salesperson without exceptional communication skills.

Have you ever wondered why some folks can solve complex math problems for breakfast while others feel their brain cells rebelling at the mere sight of numbers? Two reasons: predisposition and laziness. If math isn’t exactly one’s best friend, with a bit of sweat and possibly tears, there is room for success.

However, the main difference for all of us is in the distance each person needs to overcome to become proficient.

There are those people who might be softly reciting the Pythagorean Theorem instead of bedtime stories. Meanwhile, others need anything from a gentle prod to a full-blown bulldozer nudge towards understanding math. And then, there are those at a steadfast zero, who could gaze at a math textbook until the end of days and not have an epiphany. Individuals vary, as do their characteristics and will. Acknowledging this diversity is key to understanding and embracing the wide spectrum of inherent traits.

Strings to weave, threads to follow

Great, we have got the groundwork laid out clearly. Now, we can move on to a solution for this chaos that could actually make a difference.

A career match occurs when the skills and interests align in a way that maximizes both personal fulfillment and financial rewards. An approach inspired by the principles of Bellman’s Equation.

Young individuals often lack significant real-life experiences and self-awareness, which can pave the way for a pivotal, potentially detrimental, One Big Mistake. Since there are hundreds of professions out there and we have limited time to find the right ONE, there has to be a solution by which we can limit our aperture to the professions that are most likely in the zone of interest. That’s where data analytics and neural networks come in.

To address the challenge of finding the right profession, we can use data analysis, artificial neural networks, and media content.

Definition: an artificial neural network is a machine learning model that emulates the function and structure of the human brain by using interconnected neurons to process information and make decisions.

Inspired by String Theory, which suggests the smallest parts of our universe are made of tiny, vibrating “strings.” In this theory, the diverse vibrations of these strings are believed to form the basis of particles and forces, hinting at a fundamental interconnectedness. I imagine a similar concept for the career-matching algorithm, serving as the cornerstone of this system.

The neural network will carefully examine and extract different “threads” from the user’s profile, like skills, interests, and traits.

By connecting these threads, the algorithm can intelligently navigate various career paths to find the ones that best align with the individual’s profile. For this to work correctly, a vectorized system of relations needs to be created and proper weights assigned; however, the technical specifics of this methodology will not be disclosed here. As a result, the neural network generates a carefully curated list of career options specifically tailored to each user as recommendations.

The envisioned platform should offer key features tailored to enhance the career exploration process:

• Personalized career suggestions that align with user profiles, reflecting their unique skills and interests.
• A detailed and frequently updated database of careers, ensuring alignment with current market data and trends.
• Guidance on career progression, providing clear pathways for advancement within chosen fields.
• Insights from professionals, delivered through video content that offers a glimpse into their daily routines and shares valuable advice from their experiences.

This setup ensures a thorough exploration of diverse professions, simplifying the search for users. It paves the way for a more interactive and immersive exploration. Additionally, there is significant potential for incorporating advanced language models to enhance the user experience further.

Introducing a tool that will leverage advanced data analysis and neural networks for career guidance could significantly enhance job satisfaction and career awareness, fostering increased productivity and economic growth. This solution could improve the alignment between job seekers’ skills and available opportunities by providing personalized career advice based on current labor market trends and individual preferences. This approach promises to elevate career trajectories and contribute to a more efficient and satisfied workforce, a key driver of productivity and economic vitality.

In brightest day, in blackest night, no profession shall escape my sight

I want to solve this problem, show the younger generation what life has to offer, and help identify the profession that best fits them.

I invite you to see what I’ve developed so far — solo, I might add. Your feedback is invaluable to me, especially critical insights that can help refine and perfect this project. I want to get this right.

Dive into my video series where I talk with professionals, threading into the essence of their work, browse the updated platform, and try out the degree true value calculator tool.

I greatly appreciate your feedback as I further develop the system; together, we have the power to make a significant impact. Aspirations don’t pay the bills, but I’m committed to making a difference; the pace at which I do is the only variable to adjust.

What do you think? Can this project make a change?

Links:

Career Exploration Project: https://www.unboundway.com/

Degree NPV tool: https://www.unboundway.com/calc

My LinkedIn: https://www.linkedin.com/in/alexkelin/

About Me: https://www.guidance-ai.com/

Project’s YouTube Channel: https://www.youtube.com/channel/UCP6zSiJbbQUsS6gNg41dpbg

My Other Article: https://medium.com/@9272107/technosapiens-the-dawn-of-prompt-engineering-9c37886635d6

Buy me a coffee: https://www.buymeacoffee.com/project_unbound

Advanced Data Analytics for Career Guidance was originally published in Level Up Coding on Medium, where people are continuing the conversation by highlighting and responding to this story.

There is a shift currently happening — the new era for software engineering. Unless you live under a rock, you might have noticed everyone talking about AI, what it can, what it can’t, what it will, what it won’t. This is not an article that embraces or torches artificial intelligence (AI); it is a story about how software engineering evolved during the last 30 years and what the future awaits for it.

Why, you might ask, is this relevant to me? — First, forewarned is forearmed. Second, the changes sweeping the tech industry today merely preview broader transformations for all. Third, turns out, my barber wasn’t quite the crowd for these revelations; heeding her advice, I’m casting my net for a more receptive audience. So grab a coffee; this is one of those reads.

I. The Hard Old Times (the 90’s)

Long, long ago, in the late 20th century, in the age of “Friends” and fax machines, the idea of “going online” was reserved for a select few, and the concept of software engineering was a craft for the determined. Back then, programming languages like C, C++, Python, and, pardon me — Java were the tools of a relatively small piece of the population. They learned it the hard way, and when they wrote infinite recursion functions, those functions would eventually return willingly. Joking aside, Good times.

Reflecting on this era from our current vantage point offers a stark reminder of how far we’ve come. As you might figure, learning to code back then was no “walk in the park”; you had to read A LOT, manually debug code, and call “the guy” who knew “a guy” who might figure out what the hell was happening in your wrack. Mistakes were common; they were expected and embraced as part of the learning curve.

The resources were limited, but the sense of discovery and problem-solving thrill were immense. YouTube tutorials, Stack Overflow, Bootcamps, and fancy IDEs were unimaginable; the reliance on books and direct human interaction for learning to code was at its zenith. Artificial neural networks, emails, Machine Learning, Bellman Equations, and Moore’s law already existed, but the time to shine was yet to come.

And then, the big dot-com bang rolled in, changing everything.

II. Will the real Slim Shady, please stand up? (00’s-10’s)

As the calendar flipped to the 21st century, and the world held its breath, expecting a millennial messiah or an apocalyptic flood straight out of a prophecy. Instead, “The Matrix” scooped up its Oscars, subtly heralding the dawn of a new era.

As the millennium bug turned out to be less dramatic, the tech world took a leap, spurred on by developments that were as revolutionary as they say. Let’s line out some notable facts for the first decade of a new century, not in any particular order:

The Social Media Explosion: Ah, social media, where we traded in our privacy for likes, shares, and the ever-elusive sense of online validation. Facebook, Twitter, and LinkedIn promised to connect us, a digital Pandora’s box that transformed the internet from a network of websites into a global village square where everyone shouted and no one really listened.

The Smartphone Revolution: The iPhone, in 2007, turned every Tom, Dick, and Harriet into a photographer, filmmaker, and, most importantly, a 24/7 email responder. Smartphones crammed the power of the internet into our pockets, ensuring we could never again experience the blissful ignorance of being unreachable.

Open Source Triumphs: The 2000s were a victory lap for open source, with the likes of Linux, Apache, and GitHub leading the charge. It was a utopian collaboration vision that worked, proving that many heads (and keyboards) are better than one. This was communal living for code, without the messy business of shared bathrooms.

Stack Overflow: In the eye of this digital storm stood Stack Overflow, a beacon for every coder who’s ever found themselves whispering sweet nothings to their computer at 3 AM, begging for their program to just work.

YouTube: Then there was YouTube, the accidental educator amongst a sea of cat videos and epic fails. It turned out that amidst the clips of skateboard accidents lay a treasure trove of knowledge, making it the go-to for DIY enthusiasts, budding programmers, and procrastinators alike.

Despite these seismic shifts, the essence of learning remained surprisingly unchanged, albeit lonelier, as the camaraderie of late-night library cram sessions gave way to solitary YouTube marathons. As the lines between work, play, and study vanished, we found ourselves in a digital haze, our connections reduced to pixels, emojis and memes.

In this era of instant everything, we swapped bus window daydreams for endless screen scrolling.

Stack Overflow outranked the textbooks, Pornhub made Hustler yesterday’s news, Netflix turned DVDs into coasters, and Google became the know-it-all overlord.

A solid chunk of that generation of coders could whip up a home server with the same ease as brewing a morning cup of coffee — perhaps with slightly more caffeine jitters and fewer user manuals. Books and face-to-face conversations to learn code? Much less. Now, it’s mainly about the internet and solo learning.

III. Want to build an app? (10’s-20’s)

As we moved to 2010s, the tech world decided it was time for a makeover, a sort of digital mid-life crisis. We got “Go,” a programming language that prides itself on being as straightforward as its name suggests. This period saw the rise of the ‘Google it’ coders — a breed of developers for whom books were more decorative than instructional. “Want to build an app?” became less of a question and more of a weekend hobby, signaling a Kodak moment for those unwilling to evolve.

The tide turned back to Neural Networks and Machine Learning, disciplines that once languished in academic obscurity, hindered by the computational constraints of yesteryears. But as silicon and software leaped forward, so did their fortunes, transforming the market landscape in their wake.

Then came the new kids on the block: Crypto, with its promise of decentralized riches; DevOps, turning server rooms into high-speed racetracks; computer vision, giving sight to the blind metal birds and self-driving chariots; and cloud computing, the invisible threads weaving our digital universe. It was less of an evolution and more of a tech rave, with every new technology trying to outdo the last.

And just when we thought we’d seen it all, the first horseman of the Tech Industry Cataclysm — COVID crashed the party, turning every home into a prison cell.

There we were, sprawled on our couches, binge-watching Google employees seemingly doing everything but work — ping pong, zoom meditation sessions, napping pods, you name it — making the tech life look like a leisurely stroll in the park. The message was loud and clear: Why slog through four years of college when a few grand could buy you a fast pass on the bootcamp rollercoaster?

Just type your print(“Hello World!”) and welcome to the six-digit brotherhood.

LeetCode and HackerRank turned into the new Colosseums for devs. The narrative almost turned the whole thing into a joke. Why bury yourself in university courses on algorithms, operating systems, robotics, and systems architecture for years when a bootcamp can inject you with the latest knowledge over a few months? It’s both a brilliant shortcut and a spectacular delusion. I’m not one to cheer for cut-rate diplomas.

In my book, a self-learner no degree beats a cheap one, hands down — because, at the very least, you’re not fooling yourself. And by “cheap,” I’m not talking about the price tag on tuition, but rather the depth and quality of the knowledge being imparted.

Sorry to shatter the illusion, but ever stopped to think why those brainiacs from the Computer Science torture chambers like MIT get a job before they even graduate? It’s because they’ve survived through ‘educational Vietnam’, they don’t just know how to write code, they see the code, Neo. While you’re doodling in your IT communications class, learning what is a Wi-Fi, they’re out there, blindfolded, carving assembly code onto the backs of energy drink cans with blunt pencils, witnessing digital abysses that would make your USB stick curl up and cry.

Speaking of elite training, the broader tech landscape presented a starkly different picture. The ensuing tech job boom looked like a gold rush, with bootcamps and online courses popping up like mushrooms after a storm, offering anyone and everyone a ticket to the code-coated promised land. In its insatiable hunger for fresh meat, Big Tech dangled the carrot of six-figure salaries to anyone who could spell ‘HTML,’ turning coding from a geek’s pursuit into the new trend of gold mining.

In this brave new world, where coding became as fancy as avocado toast, we couldn’t help but wonder: were we building a digital utopia, or just inflating a bubble big enough to put the dot-com bust to shame? Only time would tell, but one thing was for sure — the tech world was having a ball, and everyone was invited to the dance.

IV. We wish you all the best in your future endeavors (20’s — now)

After roughly 2 years of work-from-home COVID times, marked by a white shirt and underwear Zoom meetings, the tech landscape began its descent. In 2022, the second horseman — layoffs, made its entrance, setting off a domino effect and initiating the grand exodus. The toll? Around 100,000 tech positions vanished in a glimpse, casualties of the industry’s adjustment to the post-COVID “new normal.”

Let’s tip our hats for a moment to the tech giants, who, caught in a hiring frenzy, hoarded coders like holiday deals, only to ditch them like Christmas trees when they had their ‘oops’ moment. The old ‘hire in haste, fire at leisure’ shuffle. Does it matter who sets the trend?

Then came the third horseman: OpenAI’s unveiling of GPT-3.5, a prototype AGI that seemed to possess genuine reasoning and thought capabilities. Developers at the time brushed it off with a chuckle, “It’s no big deal, this thing’s not replacing us. Nothing to worry about, it can’t write code, lol.” Little did they know…Meanwhile, the tech titans are sweating bullets, pouring millions into projects trying to replicate what some relative to them — a garage startup achieved with pocket change and pizza. And what does Google show for it? An AI dishing out pancake recipes, but ask it anything more complex, and it’s like talking to a brick wall. Office ping pong might have been a bad idea.

In about 5 months, brandishing the General Purpose Transformer 4.0 by OpenAI, armed with the uncanny ability to read, write and edit code. This revelation required a moment — or several — to fully sink in, especially when paired with Co-pilot’s jaw-dropping prowess. The collective response from the market? A nervous chuckle and a McConaughey-esque “alright, alright, alright,” followed by a dismissive, “It only writes junior-level code, nothing to lose sleep over.”

But here’s the kicker: if GPT-4.0 can outcode any fresh meat entering the market, what’s the verdict? “Adios” to the majority of junior roles.

The logic is irrefutable: why fork out $60–110k for a newbie to churn out and pore over basic code when a near-costless AGI can do it, perhaps with more flair and fewer coffee breaks? The new norm swiftly morphed into replacing a gaggle of juniors with a single seasoned dev who can harness such tools for superior outcomes. It’s just business.

Now, the game’s changed. Companies don’t need a herd of juniors; they need a seasoned dev who can wield AI tools. The bar for “junior” has vaulted sky-high, demanding skills and experience once reserved for an entire IT department. The new normal? “Entry-level” positions with prerequisites that would previously make a mid-level developer sweat: three years of experience, at least bilingual in code, a mastery of a dozen frameworks, and a GitHub portfolio brighter than the shiniest tile in your bathroom. Now, to even get a foot in the door, you needed a portfolio as diverse as a tech conglomerate, a resume sprinkled with cloud projects, and the problem-solving prowess to tackle a hefty chunk of LeetCode’s best hits.

The tech hiring landscape transformed into a battleground, with sweeping layoffs whetting the competition to a lethal sharpness. In the tumultuous year of 2023, the arena tallied a staggering 262,735 tech warriors fallen. Bellman’s equation is the new hiring mantra: minimize costs, maximize output, and cut down the steps to execution. The tech giants, in their quest for efficiency, had turned hiring into a cold, calculated Hunger Games tournament.

With the arena as cutthroat as it is, Mark Zuckerberg’s words echo louder: “But in some ways, actually becoming leaner kind of makes the companies more effective.”

And finally, March 2024, the fourth horseman made its grand entrance, the latest update and an ironic season finale:

So, what’s next for the aspiring tech newbie? That’s the million-dollar question, urging a generation of juniors to ponder deeply on how to navigate a market that’s evolved beyond recognition. Because at the end of the day where will you get your mid-level developers, if you don’t train new? The most stubborn survive.

This seismic shift blew through the market like a wrecking ball, leaving behind a vast oversupply of tech talent scrambling for scraps. Picture this: ex-FAANG layoffs duking it out for intern spots, bootcamp grads pouring in monthly, a bulk of affordable employees coming in from India, and the fresh-faced university alumni — all converging on a market that’s quaking like a leaf. Welcome to the brave new world, Indeed.com.

V Those who want respect — give respect.

Ironically, developers are among the first to feel the AI sting as the technological tide washes away the novices, leaving only the seasoned to navigate these choppy waters. But it’s not just developers feeling the heat; there’s a clear rule: if a job can be automated or involves repetitive tasks or requires a tangible knowledge base to operate on, it’s only a matter of time before AI takes over. This won’t happen overnight, but slowly, first the newcomers, then ,if not prepared — you.

The tremors of AGI-induced anxiety ripple across professions. Accountants, lawyers, journalists — take note. The formula remains unchanged: excel in your field, embrace AI as an ally, and you might just weather the storm. But for those whose foundations are shaky, a moment of introspection is overdue. The hard truth? Either you’ve got the nerve to battle through the blaze or you’re on the lookout for a backup plan. Just remember, in the past four years, the Biden administration has swung open the gates to over 7 million eager work seekers, with software engineers also in the mix, all jostling for their share of the job market pie. And yeah, the outlook might not be as rosy as we’d hoped. But there is a way — evolution.

This brings us to Prompt Engineering — a discipline you might already be familiar with, or perhaps we’re about to break the ice. Prompt Engineering isn’t just about asking; it’s about asking smartly and crafting concise quests that lead to precise outcomes. Let’s engrave the definition here: “Prompt engineering involves crafting inputs that effectively guide AI models to produce desired outputs or behaviors.” It’s the new frontier where the quality of your questions determines the caliber of your results, transforming how we interact with AI and making it an indispensable ally.

And learning to code has never been more accessible than it is today. It’s a world of copy-paste, ask a question to AI, and voila — an instant response. Sure, it might derail at times, and yes, errors do creep in. However, the trick lies in mastering the art of inquiry, much like the early days of becoming proficient with search engines. When I began my coding journey, my mentor imparted wisdom: “If you want to become a developer, you must learn how to Google effectively.” Initially, it puzzled me, but with time, the profound impact of phrasing questions correctly became apparent.

Traditional coding now shares the stage with Prompt Engineering, which casts Natural Language in a starring role. Andrey Karpathy, a visionary in AI, aptly noted, “The hottest new programming language is English,” highlighting his profound insight into the evolving interface between humans and AI, where mastery in conversation becomes key. This paradigm shift heralds a new internet epoch, demanding adaptability as a prerequisite for survival.

The message is clear: mastering communication with AI is crucial. If the output is gibberish, the fault lies not in the machine but in the manner of your questioning (assuming AI is fine-tuned). Mastering this dialogue requires an understanding of the machine’s “thought” process, which, at its core, is grounded in statistics, probability, and, yes, the daunting realms of mathematics. Gaining insight into the mathematical concepts that power these systems can give you a considerable edge in maximizing AI potential. Remember, as of yet, AI is a huge pool of vectors. Understanding this can transform your approach, turning seemingly inscrutable AI responses into valuable insights and actions. In this era, the mantra “Adapt or Die” rings truer than ever. Those armed with knowledge and the willingness to evolve stand to thrive, while the uninformed face the stark reality of becoming obsolete.

What should we do? Immerse yourself with AI, learn its language, and master the art of prompts. If it is already taking jobs, for starters, code generation, image generation, and now video generation — it is worth exploring. Ultimately, it is a tool, and only those who use it will be on the top of the food chain. No YouTube tutorial can fully encapsulate the essence of its work; it’s through hands-on practice and a solid grasp of the underlying mathematics that you’ll truly harness the power of AI. Once you begin to see the “patterns,” you are on the right track. In this era, change doesn’t knock; it bulldozes through your front door.

In essence, the formula for you remains the same, like the Bellman equation you all know now: maximize rewards with minimal loss and the fewest steps possible, wrapped in a layer of fulfillment if possible.

Is venturing into software engineering rewarding, intellectually stimulating, and brimming with opportunities? Absolutely. Will there always be a demand for master professionals who genuinely understand their craft? Without a doubt. But does the market and economy require the same volume of tech talent as before? Not quite. Weigh your capabilities and evaluate your odds carefully.

So here we are, at the dawn of the Technosapien era; remember this moment: look into the mirror and be honest with yourself: are you the one who rows or flows?

Good luck.

Alex

Links:

LinkedIn: https://www.linkedin.com/in/alexkelin/

About me: https://www.guidance-ai.com/

My Passion Project: https://www.unboundway.com/

My Videocast: https://www.youtube.com/channel/UCP6zSiJbbQUsS6gNg41dpbg

Technosapiens: The Dawn of Prompt Engineering was originally published in Level Up Coding on Medium, where people are continuing the conversation by highlighting and responding to this story.