If you run AI prompts often, tokens cost money. The bill adds up faster than most people expect.
What you may not know: most teams waste around half of their tokens on prompts that fail on the first attempt. The model gives back a bad answer. You rewrite. You try again. By the time you get something usable, you have run the same task three times.
This is not about your provider's per-token price. It is about the gap between vague instructions and a prompt the model can actually act on.
The pattern that wastes tokens
A typical bad-prompt cycle looks like this:
- Vague prompt. 1,000 tokens spent.
- Generic output that misses the goal.
- Rewrite the prompt. 1,000 more tokens.
- Output is closer but still off.
- Rewrite again. 1,000 more tokens.
- Finally acceptable.
Total: 3,000 tokens to get one usable answer. Success rate per attempt: roughly 33%.
A good prompt that scored well on a rubric usually lands on attempt one. Same task, same model, one third the spend.
Why prompts fail
After looking at thousands of prompts run through our QA tool, the same five mistakes account for most of the waste.
Missing constraints
"Write a blog post about AI."
What length? What tone? What audience? What specific aspect of AI? Without constraints, the model fills in the blanks however it wants, and most of those guesses miss your intent.
Fix: state length in words, tone ("plain English, no jargon"), audience ("technical founders"), and one or two scope guardrails.
No format spec
"Summarize this report."
Bullet list? Two-paragraph executive summary? Table? The model picks something. It is usually wrong for whatever you are going to do with the output next.
Fix: name the output shape. "Return a JSON object with keys summary, risks, next_actions. Each is a string under 200 characters."
No persona or buyer anchor
"Write a sales email."
To whom? About what? Solving which problem?
Fix: anchor on a real persona and a real pain. "Write to a SaaS marketing director whose lead-to-MQL ratio dropped 30% last quarter. We sell intent-data scoring."
No example
The biggest single token-saving move: include one good example of the output you want.
Without:
Translate these support tickets to Spanish.
With:
Translate these support tickets to Spanish.
Example: Input "My account is locked, please help." Output "Mi cuenta está bloqueada, por favor ayuda."
Now translate the following: ...
The second prompt usually lands on attempt one. The first one almost never does.
Instructions and content mixed together
Pasting transcripts, code, or article text inline with your instructions confuses the model about what to do versus what to operate on.
Fix: use clear delimiters.
You are an expert legal editor.
Rewrite the contract below to remove passive voice.
Return only the rewritten contract.
CONTRACT:
"""
<the actual contract text here>
"""
The triple-quote block tells the model "this is the input." Spend per attempt drops sharply because the model stops echoing the instruction text in its output.
What this is worth on a real team
A team running 200 prompts a day at average 1,500 tokens each on Claude Sonnet:
| Scenario | Tokens / day | Monthly cost (rough) |
|---|---|---|
| No QA, 33% first-pass success | 900,000 | ~$2,700 |
| QA-reviewed, 90% first-pass success | 300,000 | ~$900 |
| Saved | 600,000 | ~$1,800 / month |
That works out to around $21,600 a year on a small team. Bigger teams scale roughly linearly.
Where FixMyPrompt fits
We do not replace your model. We sit in front of it.
- Paste your draft prompt and your goal.
- The QA scores it out of 100, lists specific issues by severity, and ships an improved prompt that fixes them.
- You send the improved prompt to whatever model you already use.
The QA itself uses a tiny fraction of the tokens the failed re-runs would have burned. Real teams see 40 to 60% reduction in their monthly AI bill, plus output they want on the first try.
A faster way to check
Run a free QA on a prompt you are working on right now. No signup. You get a structured report (score, issues, rewritten prompt) in under ten seconds.
If the rewrite saves you one re-run this month, you are already ahead.