I see it every day. Someone opens a chat window and types: “Write me a marketing strategy.” They get three paragraphs of generic bullshit that sounds like it was pulled from a “Management 101” textbook, and then they triumphantly announce on Slack: “I tried it, this AI is stupid. It’s useless.”

No, the AI isn’t stupid. We’re lazy.

For the last 20 years, we got used to Google. We learned to toss short, sloppy keywords at the search bar, expecting it to guess what we meant. And now we’re trying to do the same thing with language models.

It’s a trap.

A prompt isn’t a “question.” A prompt is an instruction. It’s a technical spec for the job you’re assigning.

Imagine you hire a brilliant, incredibly fast intern who has read the entire internet but has absolutely zero context about your company, your goals, your boss, or the project you’re trying to deliver. If you throw a note on his desk that says “do a strategy,” he’ll… do a strategy.

You have to stop asking it. You have to start instructing it.

Four Things That Will Change Everything

In my daily work, I’ve boiled this down to a few iron-clad rules. If you want to stop getting garbage responses, start here.

The Magic of Role-Playing

The first thing I do in 90% of cases is give the model a persona. I stopped asking “write me an email to the client” and started writing:

“You are a senior account manager with 10 years of experience in B2B SaaS. You have a very good relationship with the client, but you must firmly but politely inform him that the additional feature X is outside the scope of the agreement and will require a separate quote. Maintain a professional but warm tone.”

Feel the difference? This isn’t voodoo. When you tell the AI who it is, you’re actually narrowing its statistical search space. Instead of looking for an answer in “the entire internet,” it starts picking words and structures typical of an “experienced manager.”

Show, Don’t Tell

This is my favorite trick, especially when I need data in a specific format. People waste time describing: “Please provide the answer in JSON format, where the first key is ‘name,’ the second is ‘status,’ and the status can be ‘active’ or ‘inactive’…”

Don’t do this. AI is a master at pattern recognition. Just show it:

“Transform the following data into JSON format according to the pattern.

Example:

Input: John Smith (Active)

Output: {“user”: “John Smith”, “status”: “active”}

Your task:

Input: Jane Doe (Inactive)

Output:”

It’ll get it in a second. You save time and get exactly what you wanted. This is called Few-Shot Prompting, and it’s absolutely brilliant for parsing logs, formatting data, or generating code.

AI as Your Personal ‘Prompt Builder’

Sometimes I don’t know the best way to ask for something. So, I use the AI against itself. This is called Meta-Prompting.

“I want to write a prompt that generates 5 creative ad campaign ideas for a new energy drink for programmers. My current prompt is: ‘Give me ad ideas.’ It’s too weak. Help me create a prompt that includes instructions on the target audience, desired tone (slightly ironic, industry-specific), and key benefits (focus, no sugar).”

The model itself will suggest how you should ask it to get better results. It’s like the tool came with its own instructor.

The Superpower: “Think Step-by-Step”

This is the most important technique if you’re facing a complex problem. AI models have a tendency to “jump” to an answer. If you give them a math or logic problem, they’ll often get it wrong because they try to give the final result immediately.

The secret is to force them to show their thought process.

If I have a complex bug in my code, I don’t ask “how do I fix this?”. I ask: “Analyze this error. Think step-by-step. Step 1: Identify what this error means. Step 2: Pinpoint the likely lines of code causing it. Step 3: Propose 3 possible fixes and explain their pros and cons.”

What happens then is fascinating. The model, by generating subsequent steps, creates new context for itself. Each new step is based on the previous one. This radically improves the quality of the answer and lets you catch errors in its own “reasoning.”

Why Does This Even Work?

You have to abandon the idea that AI “understands” the world. It doesn’t. It is, to put it simply, the world’s most sophisticated autocomplete. It’s an engine that, based on billions of texts, predicts which word is statistically most likely to appear next.

Your prompt is nothing more than setting the course for that engine.

• When you give it a role (“You are a lawyer”), it’s like you’re telling it: “From now on, only consider the statistics you found in legal texts.”
• When you give it an example (Few-Shot), you’re saying: “I don’t care what’s most probable globally. Right now, you must follow this exact pattern.”
• When you tell it to think step-by-step (Chain-of-Thought), you’re not letting it take shortcuts; you’re forcing it to build the answer slowly, token by token, where each new token is grounded in the previous one.

Stop Being Frustrated. Start Experimenting.

The internet is full of “ideal prompt checklists.” People are selling courses on “50 magic formulas.” The truth is, there is no single magic formula.

The only thing that matters is iteration.

My workflow with AI never looks like this: I write one perfect prompt, I get a perfect answer. My process is:

1. I write a quick, dirty prompt just to see “where the model is at.”
2. I get a response (usually mediocre).
3. I analyze: Why did it answer that way? Ah, because I gave it no context.
4. I iterate on the prompt: “OK, try again, but you are a data analyst and focus on trends from the last 6 months.”
5. I get a better answer, but the formatting sucks.
6. I iterate again: “Good, now format this as a Markdown table.”
7. It’s only on the third or fourth try that I get what I need.

This isn’t casting spells. This is engineering. It’s a process of calibration.

So if you’re still frustrated with the results you’re getting, I have one piece of advice: stop blaming the tool. Start writing better instructions.