You can read all the theory you want about prompting. The fastest learning comes from recognizing the specific things that reliably produce bad output — and training yourself to fix them before you send.
These are the ten mistakes I see most often. Most beginners make at least half of them. Work through each one, check your own prompts, and the improvement is usually immediate.
Mistake 1: The One-Word Task
What it looks like:
"Summarize." / "Write an email." / "Explain this."
Why it fails: The model has no idea of length, audience, tone, format, purpose, or what specifically matters to you. It will guess — and guess wrong.
The fix: Every prompt should answer: what, for whom, in what format, and why.
Summarize this article in 3 bullet points for someone who has 30 seconds to read it.
Focus on the practical implications, not the background.
Mistake 2: Asking for Everything at Once
What it looks like:
"Write a blog post, make it SEO-friendly, include examples, add a CTA, and also give me some social media posts and a subject line."
Why it fails: The model juggles too many directives and often shortchanges several of them. Complex outputs need focused attention.
The fix: Separate big tasks into sequential prompts. Get the blog post right first, then ask for social media posts, then the subject line — each in its own prompt.
Mistake 3: Vague Quality Words
What it looks like:
"Make it more professional." / "Make it better." / "Improve the tone."
Why it fails: "Professional," "better," and "improved" mean different things in different contexts. The model picks a definition you might not agree with.
The fix: Describe the actual quality you want in observable terms.
Remove all contractions, replace slang with standard vocabulary, and make
sure every sentence is a complete grammatical sentence. Keep the content
the same.
That's what "more professional" actually means for your specific use case.
Mistake 4: Forgetting to Specify Length
What it looks like:
"Explain how neural networks work."
Why it fails: The model might give you two sentences or twenty paragraphs. Both could technically be correct answers to your request.
The fix: Always specify approximate length when it matters.
Explain how neural networks work in 150–200 words. Assume the reader is
smart but not technical.
Mistake 5: Asking for an Opinion Without Wanting One
What it looks like:
"Is my business idea good?" [expects validation] "What do you think of my essay?" [expects praise]
Why it fails: Models are trained to be agreeable. Without explicit instruction, they'll validate more than they critique. You think you got honest feedback; you got diplomatic hedging.
The fix: Ask explicitly for critical analysis.
Review my business idea as a skeptic, not a cheerleader. What are the
three most serious problems with it? Don't soften the critique.
Mistake 6: No Format Instructions
What it looks like:
"Give me ideas for my product launch."
Why it fails: You might get a wall of prose when you wanted a numbered list. Or bullet points when you needed a structured table you can put in a slide deck.
The fix: Say exactly what format you need.
Give me 8 product launch ideas. Format as a numbered list.
One sentence per idea — no explanations needed.
Mistake 7: Treating AI Like a Search Engine
What it looks like:
"Best restaurants in Austin." / "Latest iPhone specs." / "Stock price of Apple."
Why it fails: AI models don't retrieve real-time information. They generate text based on their training data, which has a cutoff date. For anything current, factual, or location-specific, you need an actual search engine.
The fix: Use AI for things it's good at — reasoning, writing, explaining, brainstorming. Use search for facts, current events, and local information.
Mistake 8: Starting Over When You Should Iterate
What it looks like:
Getting a mediocre response → closing the window → opening a new chat → typing a similar prompt → getting similar mediocre results → repeat.
Why it fails: Every new conversation loses all the context you built in the previous one. You're restarting from zero instead of building forward.
The fix: Stay in the conversation. Tell the model what's wrong with its last response and ask it to fix that specific thing. It already has all the context — use it.
Mistake 9: Accepting the First Draft
What it looks like:
Getting output → copy-pasting it immediately → using it as-is.
Why it fails: First responses are first drafts. They're the model's best guess at what you wanted. That guess is often close but rarely perfect.
The fix: Read the response before using it. Ask: what's slightly off? Then do one iteration to fix that specific thing. Even one round of refinement usually gets you noticeably better output.
Mistake 10: Ignoring What the Model Told You
What it looks like:
The model says: "I'd need more information about X to answer this well" → you ignore it → you ask the same question again → same generic answer.
Why it fails: When the model asks for context or flags ambiguity, it's telling you exactly what's missing from your prompt.
The fix: When the model hedges, asks a clarifying question, or gives you a range of answers with "it depends," that's a cue. Provide the missing piece it asked for.
[After model says "it depends on your audience"]
My audience is marketing managers at B2B software companies,
5–10 years of experience, who are already familiar with CRM tools.
The Pattern Behind All 10 Mistakes
Every mistake on this list comes from the same root cause: expecting the model to fill in blanks you haven't filled in.
The model generates text based on what it's given. The less specific your input, the more it fills with defaults — and defaults are the average of everything it's seen, which is the definition of generic.
More specificity, more context, more constraints = better output.
Quick Reference: The 10 Mistakes
- One-word tasks with no context
- Too many requests in one prompt
- Vague quality words ("better," "more professional")
- No length specified
- Asking for honest feedback without requesting it explicitly
- Missing format instructions
- Using AI as a real-time search engine
- Starting fresh when you should iterate
- Accepting the first draft without refinement
- Ignoring what the model asked for
You've now completed the Beginner Track. You have the foundations: what prompts are, how to be specific, how to assign roles, how to format output, how LLMs work, how to give context, how to iterate, and the mistakes to avoid.
The Intermediate Track → builds on these foundations with more powerful techniques: few-shot examples, XML structure, chain-of-thought reasoning, and how to control AI output at a much finer level.