Prompt Engineering—Do We Still Need It? or has it become just another buzzword?

A few years ago, when large language models (LLMs) like GPT-3 started becoming popular, "prompt engineering" emerged as a must-have skill. Everyone said it was essential. If you could write the right words, phrases, and instructions, you could unlock amazing results from AI. It felt almost like magic.

But let's step back for a moment. Remember when Google first became popular? To get good results, you had to learn all these complicated tricks like using "AND," "OR," and "NOT." Knowing these made you feel like you had special powers. But soon, Google got smarter with semantic search. It started understanding exactly what you meant without all those complicated tricks. Today, I don't even remember when the last time I used these Boolean operators.

The same thing seems to be happening now with prompt engineering. Originally, you had to carefully structure prompts, set roles clearly, and explicitly state every detail. It was necessary because earlier AI models struggled to understand vague instructions. But today, models like GPT-4.5 or Claude Sonnet 3.7 are incredibly advanced. They usually understand exactly what you're asking for without special tricks.

This leads us to an important question: Do we still need "prompt engineering," or has it become just another buzzword?

The word "Engineering" is catchy!

Recently, I see many courses and workshops on prompt engineering. While the concept sounds impressive (especially because they use the word "engineering"), it makes me wonder if it's really just marketing. Engineering implies deep, structured, technical expertise. Does simply knowing how to talk clearly to an AI model qualify as engineering?

Don't get me wrong—writing clear instructions is helpful, but it doesn’t always require a special course. Most people can learn it naturally, just through practice.

In fact, here's something interesting: When I need a complex or technical prompt, like creating an advanced image prompt for SORA or writing a business case analysis, I actually ask another AI, like ChatGPT, to write it for me. And the results are usually perfect—far better than what I could create on my own.

For example, when I asked Claude to create a prompt for a comprehensive business case analysis, it generated something incredibly detailed:

This kind of advanced prompt was created effortlessly. I didn’t have to take any specialized course to learn how to do this.

The takeaway here is clear: The more advanced AI becomes, the less we need specialized "prompt engineering." Instead, we should focus on clearer communication and practical AI skills rather than temporary buzzwords.

The more advanced AI becomes, the less we need specialized "prompt engineering."

Zero-Shot Learning vs. Prompt Engineering

Modern LLMs are becoming exceptionally good at zero-shot tasks, significantly reducing the need for structured, engineered prompts. This makes natural language instructions increasingly powerful and effective, further emphasizing that specialized prompt crafting is less essential than it once was.

Let’s briefly explain zero-shot learning and how it differs from traditional prompting. Zero-shot learning refers to an AI’s ability to handle tasks without explicit examples or prior training—simply through natural language instructions. Traditional prompting, meanwhile, typically requires explicitly detailed instructions crafted by the user.

The takeaway here is clear: The more advanced AI becomes, the less we need specialized "prompt engineering." Instead, we should focus on clearer communication and practical AI skills rather than temporary buzzwords.

Unfortunately, business and marketing trends sometimes exploit the current AI transition. Many newcomers are encouraged to invest time and money into learning prompt engineering when they could be better served by focusing on more productive AI skills

NLP, LLMs, and the Original Vision

To understand where we are today, it's important to revisit the foundations of artificial intelligence. In 1950, Alan Turing published his groundbreaking paper "Computing Machinery and Intelligence." His core idea was simple yet revolutionary: machines should be able to understand and interact with humans in natural language, without needing specialized commands or engineered prompts.

Turing didn't imagine a world where users needed to craft complex input formulas. Instead, he envisioned natural, human-like conversations with machines. This vision laid the foundation for Natural Language Processing (NLP) and the entire evolution of language models.

Today, as LLMs grow more advanced, we are finally realizing Turing's dream. AI models now strive to understand what users mean, not just what they explicitly say. This is why, for the vast majority of everyday tasks, all you need is clear English—or your native language—to interact effectively.

Unfortunately, business and marketing trends sometimes exploit this transition. Many newcomers are encouraged to invest time and money into learning prompt engineering when they could be better served by focusing on more productive AI skills: understanding how AI can be integrated into their workflows, critical thinking about AI outputs, and creative problem-solving with AI assistance.

We still need good prompts for specific cases, like technical content generation or highly specialized tasks. But for 90% of daily work, simply knowing how to express yourself clearly is more than enough.

Practical Advice: Focus on Communication, Not Hype

As we look ahead, the best investment anyone can make is in improving their ability to communicate ideas clearly and thoughtfully, rather than chasing buzzwords like "prompt engineering."

If you want to become better at using AI:

• Focus on writing clearly and precisely.

• Learn to break down your needs and explain them simply.

• Develop a habit of critical thinking—ask good questions and verify AI outputs.

• Practice creative problem-solving by collaborating with AI, not just commanding it.

For newcomers, instead of spending time and money on courses that teach you how to "engineer" prompts, invest in understanding AI capabilities, limitations, and integration into real-world applications. Improving your English (or your primary working language) and learning how to structure your thoughts effectively will offer a far bigger return on investment.

AI is moving toward a future where machines truly understand us. The real advantage will not come from mastering an artificial prompting language, but from mastering clarity, logic, and creativity in our own.

So, next time you hear about a prompt engineering bootcamp, ask yourself:

Is my time better spent learning "prompt hacks"—or learning how to express my thoughts and ideas more clearly?

Because in the world of advanced AI, it's the clarity of your mind, not the complexity of your prompt, that will make all the difference!