28% of a system prompt was pure waste, and not a single rule had to go. That’s the number from a r/PromptEngineering post by u/behrouzbk. They audited a 431-token support-bot prompt and cut it to 310 tokens. Every rule and every fact survived.
The reason it matters is simple. In their test, the system prompt made up 98 to 99% of each request. You pay for it on every call, so a small trim gets multiplied by every message your bot ever sends. Picture a bot handling 10,000 conversations a day. Saving 121 tokens per request means 1.21 million fewer input tokens daily, without touching a single behavior. That’s also a little less latency on every reply, since the model has less to read before it starts writing.
Where the 121 tokens went
The breakdown is short, and it will look familiar:
- The returns policy was pasted twice: 53 tokens
- Pretty-printed JSON for the output format: 23 tokens
- “Standard shipping takes 3 to 5 business days.” stated twice: 11 tokens
- “Remember to always respond in English” right after “Always respond in English”: 8 tokens
- Politeness filler like “I would like you to please…”, “As an AI language model…” and “Thanks in advance!”: 26 tokens
That adds up to 121 tokens. Notice what’s missing from the list: no rule was weakened and no fact was dropped. Everything removed was either a duplicate or noise.
The biggest single item is the duplicated returns policy, at 53 tokens. That’s 44% of all the savings from one copy-paste. It usually happens when a policy gets pasted into a “rules” block first, then pasted again into a “knowledge” block later, because two people edited the prompt at different times. The scariest version of this is when the two copies drift apart. If one says 30 days and the other says 14, the model has to guess, and your customers get inconsistent answers.
The author says the one they see most often in real prompts is the repeated rule. Someone sees the model ignore an instruction once, adds “Remember to…”, and the patch stays forever. Nobody goes back to check whether the original line was ever the problem.
Three ways to use this
- 🔍 Run a duplicate hunt. Paste your system prompt into a doc and search for your key nouns: “returns”, “shipping”, “language”, “format”. If a fact shows up twice, keep the clearer version and delete the other. Also search for words like “always”, “never” and “remember”. Those are the usual homes of patched-on rules.
- 🧹 Strip the ceremony. Delete the greetings, the “please”, the “As an AI language model” opener and the sign-off. The model doesn’t need manners in its instructions. State the task and the constraints. A line like “I would like you to please answer questions about orders” becomes “Answer questions about orders.” Same meaning, a third of the tokens.
- 📦 Compact your formats. If your output format is a pretty-printed JSON example, try a single-line version or a short field list. Whitespace and indentation still cost tokens. Something like
{"answer": "...", "needs_human": true|false}on one line tells the model the same thing as a ten-line indented block.
Tips and pitfalls
- Test before and after. Run the same 20 or so real conversations through both versions. If outputs match, ship the shorter one. Cutting is only safe if you check. Include a few awkward cases, like an angry customer or a question outside the policy, since edge cases are where a lost rule shows up first.
- Find out why the “Remember to…” line exists. If a rule was ignored, the fix may be moving it higher, rewording it, or giving one example. Stacking a second copy usually doesn’t help.
- Don’t cut the examples that carry behavior. A repeated fact is waste. A single good example of tone or format is doing real work.
- Make it a habit. Prompts grow by accretion. A quick audit every time someone adds a line keeps the pile from forming. Keep the prompt in version control so you can see who added what, and why.
One caveat: this is a single 431-token test prompt from one person’s experiment. Your savings will depend on how messy your prompt is. A prompt that was written carefully may only shrink a few percent.
Your turn
Open your longest system prompt today and do the duplicate hunt. It takes ten minutes, and the savings repeat on every call. Then ask yourself the question the author asked the community: what’s the most common waste you find in system prompts? I’d bet the repeated “Remember to…” is near the top.
Your system prompt is paid on every call. I cut one by 28% without removing a single rule.
by u/behrouzbk in PromptEngineering