As more IT professionals adopt tools like ChatGPT, Claude, and Copilot, the ability to write effective prompts is quickly becoming a core digital skill. But while the idea of prompting seems simple, "just ask the AI a question" the reality is more nuanced. Beginners often make avoidable mistakes that lead to poor responses, irrelevant output, or frustrating trial and error.
In this blog, we’ll break down the most common mistakes made while learning prompt engineering and explain how to fix them. To get better results from GenAI tools, avoid these mistakes. This will help you write clearer prompts and reduce errors.
Related: Read the full guide → Generative AI & Prompt Engineering for IT Professionals
Table of Contents
- Discovering the Most Popular Mistakes in Prompt Engineering
- Mistake #1: Writing Prompts That Are Too Vague
- Mistake #2: Forgetting to Specify Format or Output Style
- Mistake #3: Not Giving Context or Examples
- Mistake #4: Asking for Too Much in One Prompt
- Mistake #5: Treating AI as Perfect or Always Right
- Mistake #6: Repeating the Same Prompt Without Tweaks
- How to Improve Your Prompt Engineering Skills
- FAQs
- Conclusion
Discovering the Most Popular Mistakes in Prompt Engineering
Most people start using AI tools with great excitement, but quickly become frustrated when the responses feel off, generic, or irrelevant. That’s because they haven’t yet learned how to speak the language of LLMs. Prompt engineering is a skill that improves with practice, but many learners fall into the same traps early on.
Let’s explore these common prompt engineering mistakes, and more importantly, how to avoid them.
Mistake #1: Writing Prompts That Are Too Vague
When you type a general question like "Write a test plan," the AI will likely generate something generic. The problem? AI doesn’t read your mind, it only responds to the information you provide.
Better Approach:
“You are a QA lead for an e-commerce checkout module. Write a test plan covering functionality, security, and performance. Use bullet points.”
This gives the AI a role, scope, and formatting expectation, leading to more specific QA-focused output.
Mistake #2: Forgetting to Specify Format or Output Style
Even if the AI understands the task, it might give you a long paragraph when you need a list, a table when you prefer JSON, or forget code formatting.
Better Approach:
Always tell the AI how you want the response. For example:
“Provide your answer in a markdown table with columns: Test Case, Input, Expected Output.”
This reduces the need to clean up or reformat responses later and improves productivity for manual testers and Python developers.
Mistake #3: Not Giving Context or Examples
Imagine asking a human, "Write a good prompt." That’s difficult without knowing what it’s for. GenAI works the same way, it performs better when you provide an example or background.
Better Approach:
“Here’s a bad prompt: ‘Summarize this’. Rewrite it to make it more specific, useful for a data analyst.”
Context leads to clearer understanding and stronger data-driven insights.
Mistake #4: Asking for Too Much in One Prompt
Long, overloaded prompts confuse the model. If you say, "Write 5 test cases, a test plan, a bug report, and create a SQL script for testing", you’ll likely get something incomplete or irrelevant.
Better Approach:
Break it down:
- First, ask for the 5 test cases.
- Then ask for the test plan.
- Then move to SQL script.
This lets the model stay focused and produce higher-quality responses.
Mistake #5: Treating AI as Perfect or Always Right
Many beginners assume that everything ChatGPT or Claude outputs is accurate. But AI can hallucinate, invent facts, or misinterpret your intent.
Solution:
- Validate outputs, especially code, test cases, or analytics.
- Ask follow-up questions like “Are there any errors in your previous response?”
- Cross-check with your own logic or documentation.
Explore how real companies avoid these issues in GenAI testing use cases.
Mistake #6: Repeating the Same Prompt Without Tweaks
If a prompt didn’t work the first time, simply re-entering it won’t help. Instead, modify the structure, add examples, or simplify the language.
Fix-It Tip:
Use Chain-of-Thought prompting:
“Step by step, explain how this Python function works before rewriting it.”
Iterating prompts improves your output and helps you learn what works.
How to Improve Your Prompt Engineering Skills
Becoming a better prompt engineer is like learning a language, you improve with feedback and practice. Here’s how to accelerate:
- Study prompt libraries and examples
- Experiment with roles, like “You are a software architect...”
- Track your best prompts
- Use prompt testing tools like PromptLayer or Playground
- Ask for critiques inside the prompt
Need help improving your skills? Talk to a mentor or check out our Prompt Engineering Course.
FAQs
Q1. What is the most common beginner mistake in prompt engineering?
Vagueness. If your prompt is unclear or lacks structure, you’ll get generic results.
Q2. How do I make my prompts more effective?
Be specific. Add role, task, format, and context. Structure your prompt like an instruction.
Q3. Should I always trust the AI’s answer?
No. Always validate outputs. Use AI as a helper, not a final decision-maker.
Q4. Can prompt engineering be learned without coding?
Absolutely. Prompting is language-based. Even non-tech users can master it with practice.
Conclusion
Prompt engineering is the bridge between humans and generative AI, but it requires clarity, context, and critical thinking. Beginners often struggle not because they lack intelligence, but because they assume AI “just knows.”
By avoiding these common prompt mistakes, you’ll start writing better inputs, getting more reliable outputs, and building job-ready AI skills faster.
Want to master prompting with real-world projects and career guidance?
Join CDPL’s Prompt Engineering & GenAI Course