Diagnosing and Fixing Bad Prompts

When a prompt produces a bad output, most people's first instinct is to blame the model. But in most cases, the problem is in the prompt — and the model is behaving exactly as it should given the instructions it received.

Treating prompt failures as debugging problems is more productive. Just like debugging code, you systematically identify the root cause, hypothesize a fix, test it, and verify the result. This mindset produces faster improvement than frustrated trial and error.

When a prompt fails, work through this five-dimension checklist systematically:

1. Specificity — Is it too vague? Does the task need more precise definition?
2. Context — Is background information missing that the model needs?
3. Format — Is the desired output format and length specified?
4. Constraints — Are constraints clearly stated? Do any of them conflict?
5. Examples — Would an example of good output eliminate ambiguity?

Each dimension maps to a specific type of fix. Identify which dimension failed and apply the corresponding fix.