Top 10 Common Prompt Mistakes

The ten most common Prompt Engineering mistakes developers make, plus practical fixes.

Mistake 1: Vague instructions

Problem: prompts such as 'make it better' or 'be more detailed' do not define success.

Fix: specify the exact improvement dimensions, constraints, and expected output format.

Mistake 2: Too many tasks in one prompt

Problem: combining unrelated tasks makes output unstable.

Fix: split the workflow into focused prompts and chain them together.

Mistake 3: No output format

Problem: the model returns a different structure each time.

Fix: define a schema, section layout, or table format and validate the result.

Mistake 4: Poor few-shot examples

Problem: examples are inconsistent, biased, or wrong.

Fix: cover categories and edge cases with clean, consistent examples.

Mistake 5: Ignoring model differences

Problem: a prompt optimized for one model is copied to another without adaptation.

Fix: adjust style, separators, context strategy, and tool call format per model.

Mistake 6: Overloaded system prompt

Problem: too many rules dilute the important behavior contract.

Fix: keep system prompts concise and move task-specific detail into user prompts.

Mistake 7: Ignoring temperature

Problem: every task uses the default sampling setting.

Fix: use low temperature for deterministic tasks and higher temperature for ideation.

Mistake 8: No prompt testing

Problem: a prompt is launched after a few manual tries.

Fix: create test cases, automate evaluation, and record changes.

Mistake 9: No safety boundary

Problem: user input is pasted directly into instructions.

Fix: isolate user input, detect injection attempts, and define refusal or fallback behavior.

Mistake 10: No cost control

Problem: token use grows without monitoring.

Fix: track tokens, use caching, choose the right model size, and remove redundant prompt text.