AI Prompt Optimizer
Cut unnecessary tokens from your prompts and save API costs
Token estimates are approximate. Manual review is recommended after optimization. Some prompts may intentionally use formal language for specific use cases.
Why Prompt Optimization Matters
Every token in your prompt costs money. For production applications making thousands of API calls per day, even small optimizations compound into significant savings. A 200-token reduction per request saves $15/month at 1K requests/day on GPT-4o - that is $180/year from a single prompt improvement.
Common Prompt Inefficiencies
Filler Words
Words like "please", "could you", "I would like you to" add tokens without adding value. LLMs respond to instructions, not politeness.
Could you please write a summaryWrite a summaryVerbose Phrases
Common phrases that have shorter equivalents with identical meaning.
in order to → todue to the fact that → becauseRepetition
Repeating the same instruction in different words. LLMs process all context, so duplicates waste tokens.
Too Many Examples
2-3 high-quality examples are optimal. More examples increase tokens without proportionally improving quality.
Cost Impact Calculator
| Tokens Saved/Request | 100 req/day | 1,000 req/day | 10,000 req/day |
|---|---|---|---|
| 50 tokens | $0.38/mo | $3.75/mo | $37.50/mo |
| 100 tokens | $0.75/mo | $7.50/mo | $75.00/mo |
| 200 tokens | $1.50/mo | $15.00/mo | $150.00/mo |
| 500 tokens | $3.75/mo | $37.50/mo | $375.00/mo |
Based on GPT-4o input pricing ($2.50/M tokens). Savings scale linearly with usage.
FAQ
How much can I save by optimizing my prompts?
Most prompts contain 15-30% unnecessary tokens from filler words, verbose phrases, and repetitions. For a production app making 1,000 requests/day with GPT-4o, saving 200 tokens per request translates to $15/month saved - just from prompt optimization. High-volume applications can save thousands of dollars monthly.
Will optimizing my prompt reduce output quality?
No - removing filler words like "please" and "could you" does not affect LLM output quality. LLMs respond to instructions, not politeness. However, removing context, examples, or specific constraints CAN affect quality. Always review the optimized prompt before deploying.
What are the most common prompt inefficiencies?
The top 5 are: (1) Filler words ("please", "could you", "I would like you to"), (2) Verbose phrases ("in order to" instead of "to", "due to the fact that" instead of "because"), (3) Repeated instructions or context, (4) Too many examples (2-3 is optimal), (5) Meta-commentary ("it should be noted that", "keep in mind that").
Should I remove all examples from my prompt?
No. 2-3 high-quality examples (few-shot learning) significantly improve output quality. The tool flags prompts with 4+ example markers as a warning. Keep your best 2-3 examples and remove the rest. Each example typically costs 50-200 tokens.
Does this tool work for non-English prompts?
Yes, the tool detects inefficiencies in both English and Chinese prompts. For Chinese text, token estimation uses a ratio of approximately 0.6 tokens per character. The optimization patterns primarily target English filler words, but Chinese prompt best practices are also covered in the analysis.