Prompt Engineering Common Mistakes

Most prompt engineering mistakes come from assuming the model knows what you know. Here are the most common problems and how to fix them.

Mistake 1: Vague Task

Before: Make this better.
After:  Rewrite this paragraph for a non-technical reader, under 60 words,
        keeping the two statistics unchanged.
(the "after" prompt defines "better")

Mistake 2: Conflicting Instructions

Before: Be extremely detailed. Keep it under 50 words.
After:  Give a 50-word summary, then a "Details" section with up to 5 bullets.
(both needs are met without contradiction)

Mistake 3: No Output Format

Before: Which of these leads are good?
After:  For each lead return one line: <name> | HOT/WARM/COLD | one-sentence reason.
Sample response:
Imran Ali | HOT | Requested a demo and has budget approved.
Lisa Chen | COLD | Student email, no company listed.

More Mistakes to Avoid

  • Burying the key instruction in the middle of a long prompt.
  • Asking for facts the model cannot know (private data, very recent events) without supplying them.
  • Using few-shot examples that all look the same.
  • Testing on one input and declaring success.
  • Overusing capital letters and threats instead of explaining the goal.
  • Putting untrusted text next to instructions without delimiters.

A Debugging Checklist

  • Could a smart new colleague do this task from the prompt alone?
  • Is the desired output shown or described exactly?
  • Are examples representative and correct?
  • Is temperature appropriate for the task?
  • Did you run it on your test set?

Frequently asked questions

Why is my prompt giving different answers each time?

Randomness from sampling and ambiguity in the prompt. Lower the temperature and make the task and format more specific.

Should I ask the AI to improve my prompt?

Yes, it is a useful starting point, but always test the suggested prompt on real examples.

Why does the model ignore one of my rules?

Rules can conflict or get lost in long prompts. Move it near the end, explain why it matters, and check for contradictions.