Prompt engineering might sound technical, but it’s about getting better results from AI tools by asking the right way. Whether you’re using ChatGPT, Claude, or any other generative AI, the way you phrase a question or task can completely change the output you get.
These tools are impressive, no doubt, but they aren’t mind-readers. A vague or poorly worded prompt can leave you with something generic or off base. Conversely, a well-crafted prompt can make the AI feel almost like a subject matter expert.
If you’re new to using AI, it’s easy to assume you just type in a question and let it do the work. But that approach often leads to frustration.
In this article, we will walk through five common mistakes beginners make when writing prompts and, more importantly, how to fix them. Once you spot these patterns, your results will improve almost immediately.
Mistake #1: Being Too Vague or Open-Ended
One of the most common mistakes beginners make is being too vague in their prompts.
If you’ve ever typed something like “Write an article” into an AI tool and ended up with a bland, directionless wall of text, you’ve experienced this firsthand.
AI doesn’t read your mind. It takes what you give it. A prompt that lacks detail often leads to a response that lacks depth.
For example, saying “Write an article” tells the AI nothing about your audience, purpose, tone, or topic. But try something like:
“Write a 500-word blog post on prompt engineering for marketers. Make it clear and slightly casual, aimed at beginners, and include a few examples.”
Now the AI has something to work with.
The fix?
Be specific. Treat your prompt like instructions to a freelance writer or assistant. Include details like format (blog post, summary, script), word count, target audience, and tone. Adding simple constraints like “in bullet points” or “no more than 100 words” can drastically improve the results.
In short, the more context you provide, the better the outcome. Consider prompting as setting the table; if you throw a plate down, dinner might not go well. But if you prep properly, you’re more likely to get a great meal.
If you’re just starting, exploring a structured Prompt engineering course for ChatGPT can help build the proper foundation early on.
Mistake #2: Ignoring the Importance of Specificity in Query Results
Another powerful but often overlooked trick in prompt engineering is assigning the AI a specific role. When you say “Act as a UX researcher” or “You are a technical recruiter writing a job ad,” you’re setting a mental context that helps guide the AI’s tone, vocabulary, and focus.
Without that context, AI responds with general knowledge or worse, generic filler. For example:
- Prompt A: “Give tips on improving user onboarding.”
- Prompt B: “Act as a senior UX designer. Give me five tips on improving mobile app onboarding for first-time users.”
The second prompt is much more likely to return practical, detailed, and relevant insights.
Why does this work?
Assigning a role helps the AI narrow its knowledge scope and apply the right lens to your request. It’s like giving it a character to play in a script; it becomes more intentional and aligned with your goals.
To apply this, start by thinking: Who would I ask this question to in real life? Then write your prompt as if you’re addressing that expert. It could be a marketer, lawyer, software engineer, therapist, or whatever fits your context.
When you give the AI a role, you’re not just telling it what to do but how to think while doing it. And that shift makes a big difference.
Learning how to frame prompts using roles and contexts is a skill that improves with guided practice, something courses like ChatGPT for Working Professionals by Great Learning are designed to support.
Mistake #3: Overloading the Prompt with Multiple Tasks
Another standard error beginners would make is overstuffing instructions in a single prompt. It is easy to comment on something like, “write a product description, summarize in three bullet points, and translate into Spanish.”
However, when one asks the AI to do several tasks in tandem, it most likely leads to one of the two outcomes: an unclear reaction, or if some part is good while the rest are not. AI works best when it’s focused.
Overloading it with unrelated or layered requests makes it harder for the model to prioritize what matters most. The output often ends up being shallow or disjointed.
Instead, try breaking complex requests into smaller chunks. Think of it as talking to a teammate; you wouldn’t ask someone to research, write, design, and translate something in a single breath. You’d go step by step.
For example:
First, ask: “Write a 100-word product description for [product], in a friendly tone.”
Then: “Summarize the above into three bullet points.”
Then: “Translate the summary into Spanish.”
This approach is called prompt chaining, and it not only gives you better results but also more control over each stage of the process. It turns the interaction into a workflow, rather than a one-shot request.
Mistake #4: Not Iterating or Refining
Many beginners assume that a single prompt should deliver the perfect result. In reality, most high-quality AI outputs come from iteration, asking follow-up questions, adjusting instructions, or refining tone and details step by step.
Imagine writing a draft yourself. The first version is rarely the final one. The same applies to AI-generated content. Let’s say your first prompt gives you a decent blog intro, but it’s a bit dry.
Instead of scrapping it, follow up with: “Make it more engaging for a beginner audience” or “Add a quick example to clarify this point.
Every refinement moves the AI in increments towards your ideal result. Consider the process like a conversation, not a vending machine where you punch in one and get precisely what you want. Here’s a quick example:
Prompt: “Write a 100-word intro to an article on time management.”
Follow-up: “Now make it sound less formal.”
Then: “Add a short stat or quote about productivity.”
Each step improves the output without starting from scratch. And over time, you’ll get faster at knowing what kind of tweaks produce the best results.
In short: don’t expect magic in one shot. The real power of prompt engineering lies in iteration: asking, improving, and shaping the AI’s response until it works for you.
Mistake #5: Ignoring the AI’s Limitations
It’s easy to forget that AI still has limits, no matter how advanced. One of the biggest mistakes beginners make is assuming the AI always “knows” what it’s talking about. But the truth is: AI generates responses based on patterns in data, not real understanding or verified facts.
For instance, asking for statistics, quotes, or legal advice might give you something that sounds right, but isn’t actually accurate. People have made the mistake of copying AI-generated answers directly into reports or proposals, only to realize later that some of it was misleading or completely wrong.
The fix? Use AI as a collaborator, not a source of truth. It’s excellent at brainstorming, summarizing, drafting, or helping you organize your thinking. But it shouldn’t replace expert judgment, critical thinking, or solid fact-checking.
When in doubt, treat outputs like a first draft or a rough idea. Cross-check important claims. If you’re writing something factual, technical, or sensitive, use the AI to speed up the groundwork but rely on trusted sources or professionals for final review.
The goal of prompt engineering isn’t to outsource your thinking, it’s to enhance it. Knowing when to lean on AI and when to question it is part of the skill.
Also Read: How to Become a Prompt Engineer?
Conclusion
Prompt engineering isn’t just about getting better answers; it’s about asking better questions. As you’ve seen, many beginner mistakes come down to a lack of clarity, structure, or strategy. But the good news is that these mistakes are easy to fix with just a bit of awareness and practice.
Let’s recap the five key mistakes:
- Being too vague – Solve it by adding specifics and clear instructions.
- Skipping role assignment – Fix it by giving the AI a defined persona.
- Overloading prompts – Break tasks into simpler, focused steps.
- Not iterating – Treat it as a process, not a one-and-done deal.
- Ignoring limitations – Use AI to assist, not replace human judgment.
If you’re ready to go beyond the basics, consider diving into a more comprehensive program like Generative AI to build long-term skills that apply across use cases and tools.
In the end, prompt engineering is less about tricks and more about thoughtful communication. The better you get at that, the more powerful these tools become.
