What Is a Prompt in AI?

I was working for a small tech startup in Serbia, chasing my dream of learning to code and building software. It was a great experience where I developed a newfound obsession with Pljeskavica (a delicious local dish). But navigating technical documentation as a non-coder felt like trying to read hieroglyphics.

I bounced between textbooks, Stack Overflow, and YouTube videos, trying to crack open the seemingly esoteric world of low-level programming to try and keep up with the bright team of builders.

When ChatGPT launched, I saw a chance to get my endless questions answered without embarrassment or wasting an engineer’s time.

My mentor always said mastery requires mastering the fundamentals. So I started by challenging the most basic concepts that every developer seemed to answer differently. "What exactly is an API?"

For the first time, I got an explanation I could actually follow. The answer was broken down to my level of understanding, using examples from my field of expertise.

I didn't know the word for it yet, but that was my first prompt.

So what exactly is prompting within the context of AI?

A prompt is the input you give an AI system to produce a response. It can be a question, an instruction, a document you want summarized, or a description of what you need. Every time you type something into an AI chatbot, you're writing a prompt.

It's one of those "five minutes to learn, a lifetime to master" concepts. Better prompting can unlock massive performance improvements in generative AI systems.

Here are the most actionable tips I've learned from spending over 2,000 hours working with AI and optimizing prompts.

How AI Prompts Actually Work

What happens when you hit send

Many people think that when you type a prompt and press enter, the AI searches a database or browses the internet. That's not what happens. The system predicts what a useful response might look like based on patterns it learned during training.

The quality of your output depends on the context you provide. The strong versions work better because they give the AI a solid starting point.

Since AI is designed to make predictions rather than searches, it can also confidently produce wrong information. This is a well-known side effect called hallucination. Vague prompts worsen this.

When you give the AI a broad topic without constraints, it has more room to fill gaps with plausible-sounding nonsense (e.g., slop). A specific prompt with clear context keeps the model focused, thus reducing the chance it drifts.

Not All Prompts Are Created Equal

Getting great outputs from AI prompts is often a skill issue

There's a common belief that people who get amazing results from AI know some kind of secret formula. A specific structure. A magic phrase. Some obscure scientific knowledge is beyond the reach of common people. But that’s simply not true.

The people getting genuinely useful results work within the constraints of how generative AI operates. You maximize the model's capabilities while minimizing its limitations through effective prompting.

I figured this feature out in the summer of 2023 when GPT-4 had just launched.

I wanted to learn about algorithmic trading, so I opened a chat and asked a broad question about how it worked. Then I asked a follow-up about a specific concept within it. In the third message, I asked the detailed question I actually cared about.

That third response was significantly better than anything I'd gotten before. The first two messages had given the AI the context it needed to understand what I was actually after. Same model. Same topic. The only thing that changed was how I set up the conversation.

I didn't have a name for what I was doing. Later, I'd learn people were calling it “context engineering,” that is, giving the AI your full situation instead of just a question. At the time, I felt as though I had discovered a real-life cheat code.

The layers most people never think about

When people think of prompting, they usually only think about what they type into their chat window. Clear question, decent structure, maybe a few details. That part is important, but it is only the starting point for proper prompting.

The people who get the best results from generative AI models think about what comes before the message they type. Here are the three things that I always consider when coming up with a prompt.

What context would help the AI answer your question? Consider your situation, your constraints, previous attempts, and the intended purpose of the output.

Someone asking for "a marketing email" is working with completely different information than someone asking for "a marketing email to re-engage customers who haven't purchased in 90 days from a small business that depends on foot traffic.”

Same task, but the second person will get a better result every time.

I do this with learning, too. When I'm studying math or science, I'll tell the AI to teach me in the voice of Einstein. For business strategy, I'll ask for Peter Diamandis. The default voice that comes out of language models is bland. Giving it a voice to work with is another layer of context that changes the output.

Details about your situation and why you need the answer. There's a gap between what people ask for and what they actually need. "Write me an email" and "I need to decline a meeting without damaging the relationship because a client deadline matters more this week" are both email requests.

One gives the AI a task. The other gives it purpose. The "why" behind a request changes everything about the response.

Clear description of what a bad answer looks like. If you don't define success, the AI guesses. And if you can describe what would make you reject the output ("too formal," "sounds like a form letter," "too long"), you narrow the possibilities faster than describing what you want.

To find out how different models perform, use chat aggregators like LorkaAI to test all the latest state-of-the-art models under one affordable subscription.

Common Mistakes That Hold People Back

Sometimes getting great results isn't about doing everything right, but instead about avoiding the mistakes that hold you back. Here are the three biggest mistakes that people make.

1. Thinking AI works like Google. This is the misconception behind most bad prompts. You type "marketing strategies" the same way you'd type it into a search bar, and you get a generic summary. But AI doesn't search an index. It predicts.

"I manage a team of five and need to reduce our client churn rate this quarter" gives it enough signal to predict something specific to your situation.

2. Assuming the AI knows what you mean. You ask for "a cover letter for a job application" and reject three drafts because none of them sound like you. The AI didn't fail. It predicted what a generic cover letter should look like because you gave it nothing else to work with.

Your tone, your experience level, and what makes you a good fit for this specific role, all of that was in your head. None of it was in the prompt.

I see this in YouTube comments and LinkedIn threads all the time. People ask each other, "What's* your prompt?"* as though there's a perfect version somewhere. There isn't. Ten people with the same task will have ten different audiences, tones, goals, and definitions of success. A prompt that works for one person's situation won't work for yours.

3. Blindly trusting the first answer without questioning it. Most AI models are agreeable by default. They're designed to be helpful, so they often agree with you, even if you're wrong.

Recently, a developer created an open-source benchmark to determine which AI models produce accurate information rather than simply agreeing with whatever you tell them.