Stop paying for filler words in every prompt
Large language models bill per token, and your system prompt and instructions are charged on every single request. Wordy boilerplate like “in order to”, “please be aware that” and “it is important to note that” adds tokens without changing what the model does — so it quietly inflates your bill across thousands of calls. Paste your prompt above and the checker flags the worst offenders and shows a leaner rewrite.
How it works
The tool scans your text for a curated list of high-frequency filler phrases that prompt engineers commonly leave in. For each match it suggests a token-lean replacement — “in order to” becomes “to”, “due to the fact that” becomes “because”, and pure padding like “kindly” or “please note that” is simply deleted. It then rebuilds a cleaned-up version of your prompt and estimates the token saving using a standard ratio of about 1.3 tokens per English word. The exact figure depends on your model’s tokenizer, but the relative reduction is what matters for budgeting.
The phrases that add the most bloat
Some filler patterns are so common in AI-written and business-writing prompts that they deserve special attention. A few examples the checker targets, with their lean equivalents:
| Inflated phrase | Lean replacement | Why it pads |
|---|---|---|
| ”in order to" | "to” | Two extra tokens every occurrence |
| ”due to the fact that" | "because” | Six words become one |
| ”it is important to note that” | (delete) | Adds no instruction value |
| ”please be advised that” | (delete) | Politeness the model ignores |
| ”I would like you to” | (delete or use imperative) | Hedging the model does not need |
| ”make sure to ensure that" | "ensure” | Redundant stacking |
These are illustrative examples of the class of phrases the checker flags — not an exhaustive list. The key insight is that they tend to cluster in prompts written in a polite, corporate-email voice that feels natural to humans but is purely overhead for a language model.
Why this compounds at scale
Consider a system prompt that is 300 tokens long and called 500,000 times a month. Shaving 50 tokens off it (a realistic saving for a wordy prompt) removes 25 million input tokens per month — potentially a meaningful cost reduction depending on the model tier. The percentage saving scales with the number of calls, not with the absolute dollar amount, which is why even modest per-call reductions become significant in high-volume pipelines.
Tips for token-lean prompts
- Be direct. Models follow clear imperatives (“Return JSON”) better than hedged requests (“I would like you to please make sure to return JSON”).
- Cut meta-commentary. Phrases that talk about the instruction (“it should be noted that”) rarely change the output — only the token count.
- Move stable context out. If a long instruction never changes, consider prompt caching so you are not re-billed for it on every call.
- Check the rewrite before shipping. The auto-generated leaner version is a starting point; verify that any replacements do not accidentally alter the instruction semantics before deploying.
- Measure at volume. A 30-token saving on a prompt called a million times a month is 30 million tokens — real money on most pricing tiers.