Do you remember that moment when you first opened ChatGPT or another neural network? You eagerly typed in a prompt like “Write a post about marketing,” only to get a response so watered-down and cliché that it felt like it was written by a robot from a B-movie.
Disappointment? You bet.
But the problem wasn't with artificial intelligence. The problem was with the prompt (request).
Today, prompt engineering is no longer considered just a fun toy for geeks. It’s a fundamental 21st-century digital literacy skill—just like knowing how to use Google, work in Excel, or write business emails. AI is like an incredibly knowledgeable but completely clueless and unmotivated intern. Without clear instructions, it’ll give you the average temperature in a hospital. With the right instructions, it’ll draft a strategy worthy of a senior consultant.
How can you turn a neural network from a generator of clichés into a powerful assistant? Let’s take a look at the two pillars of effective prompt engineering: context and constraints.
Context: Enabling AI "expert mode"
Imagine you’re hiring a copywriter. You wouldn’t just tell them, “Write,” would you? You’d explain who the target audience is, what the company’s brand voice is, what the product is all about, and what the text is supposed to accomplish. The same magic applies to a neural network.
Context answers the following questions: Who is speaking? To whom? Why? Under what circumstances?
How to set the context:
- Role (Persona): Give the AI a point of view. “Imagine you’re an experienced manager with 10 years of experience in fintech…” or “You’re a strict editor of a tech blog…”
- Target Audience: Who is this content intended for? “This text is intended for startup founders who know nothing about SEO but want to attract investors.”
- Background and Context: Provide some background information. “We’re opening a third-wave coffee shop in a residential neighborhood of a major city. Competition is fierce. Our marketing budget is limited.”
"Before and after" example:
- Bad: “Write a marketing plan for the coffee shop.”
- Okay: “You’re a marketer with experience in the local foodservice industry. Our goal is to develop a marketing plan for a new coffee shop in a residential neighborhood on a minimal budget. The target audience is young remote workers and moms with kids. Come up with 5 creative ideas.”
Limitations: We set boundaries for AI (so it doesn't run off into space)
Neural networks love to let their thoughts branch out like a tree. If you don’t set limits on the model’s length, format, or style, it will spit out a massive wall of text from which you’ll have to fish out bits of meaning one by one. Constraints are your sculptor’s scalpel.
Restrictions answer the following questions: What should you NOT do? What exactly should the finished product look like?
Which restrictions work best:
- Length: Specify the length clearly. “No more than 1,000 characters,” “Exactly 3 paragraphs,” “A list of 5 items.”
- Stopwords and clichés: AI is guilty of using corporate jargon (“rapidly growing,” “synergy,” “in the era of digitalization”). Ban them! “Don’t use bureaucratic jargon, filler words, or introductory phrases like ‘in conclusion, I would like to note.’”
- Format: Specify a specific structure. “Provide your answer in a table with three columns: Task, Tool, Deadline” or “Use Markdown formatting with H2 subheadings and a bulleted list.”
- Tone of Voice: “Write in short sentences, without enthusiastic exclamation marks, in the style of a popular science magazine.”
"Before and after" example:
- Bad: “Explain blockchain in simple terms.”
- Okay: “Explain what blockchain is to someone who has never encountered cryptocurrencies. Use a real-life metaphor (for example, a school diary or a shared notebook). Restrictions: up to 500 words, no complex technical terms, and do not use the word ‘decentralization.’”
The Formula for the Perfect Prompt
If we combine context and constraints, we get a universal formula that can be used to construct any query:
[Role] + [Context/Task] + [Target Audience] + [Format and Constraints] + [Example of the Desired Outcome (optional)].
Checklist for reviewing your prompt:
- Did the neural network figure out who it's supposed to be?
- Did I provide enough background information on the issue?
- Did I specify the format in which I want to receive the result?
- Have I used restrictions to keep the text free of "fluff" and bureaucratic jargon?
In lieu of a conclusion
Prompt engineering isn’t the same as programming in Python. It’s more of an art of effective communication and delegation. The more precisely you articulate your thoughts to the neural network, the better you begin to understand exactly what you want yourself.
