TL;DR: You can strip 30-40% of your prompt tokens without changing what the prompt actually does. The method takes about two minutes.
What “Semantic Shorthand” Means
A post in r/PromptEngineering shared a technique called Machine-Readable Logic Seeds. The core idea: rewrite any wordy prompt using only imperative verbs, zero articles (the, a, an), and technical abbreviations.
The target is under 150 tokens with 100% of the original logic intact.
It’s writing for the model, not for a human reader. Models don’t need “the” or “a.” They don’t need filler. They need instructions.
Here’s a quick example. A typical system prompt might say: “You are a helpful assistant that analyzes the user’s input and provides a structured summary of the main points in the text.” That’s 31 tokens of friendly, readable prose. The Machine-Readable version looks more like: “Analyze user input. Extract main points. Return structured summary.” Eleven tokens. Same output.
The difference feels wrong at first because we’re trained to write for human readers. But the model doesn’t need the scaffolding. It processes the instruction either way. Stripping the scaffolding just makes it cheaper to run.
There’s also a side benefit worth calling out: compressed prompts tend to be more precise. When you’re forced to use only imperative verbs and drop the filler, you can’t hide vague instructions behind polite phrasing. You either know exactly what you want the model to do, or the compression process reveals that you didn’t. Either way, you come out ahead.
Why This Matters at Scale
Single API calls? Token bloat is annoying. Thousands of calls per day? It compounds into real money.
System prompts that run on every request are the obvious first target. A 200-token system prompt that becomes 120 tokens is a 40% cut on every single call.
Let’s put a real number on it. If you’re running 10,000 API calls per day with a 200-token system prompt on Claude Sonnet, that’s 2 million tokens of system prompt overhead per day. Cut that to 120 tokens and you’re at 1.2 million. At current pricing that’s roughly $2.40 saved daily, around $876 per year, from one prompt rewrite. Now multiply that across a product with multiple agents, each running their own system prompts, and you’re looking at a meaningful chunk of your infrastructure budget recovered from thin air.
Prompt caching helps, but it doesn’t make bloat free. You still pay for uncached calls, cold starts, and cache misses. Compression reduces the ceiling on what you can spend, cached or not. It’s a floor, not a patch.
Use Cases
- 💡 Static system prompts (classification, routing, extraction)
- Summarization pipelines with fixed output formats
- 🔁 Any agent loop where the same prompt runs hundreds of times
Classification prompts are usually the best candidates. They tend to be verbose because whoever wrote them was thinking about readability for teammates, then copy-pasted the whole thing into production. A routing prompt that sorts support tickets into categories doesn’t need full sentences. It needs clear rules, imperative verbs, and nothing else.
Extraction prompts are a close second. If you’re pulling structured data from documents, your prompt probably has a lot of context-setting that the model doesn’t actually need. The model already knows what JSON is. You don’t need to explain it every time.
Agent loops deserve special attention. If the same base prompt fires 500 times to process a batch job, every token in that prompt is multiplied by 500. Compressing a 300-token prompt to 180 tokens saves 60,000 tokens per run. Across a week of daily batch jobs, that adds up fast.
Prompt of the Day
“Rewrite these instructions into a Machine-Readable Logic Seed. Use imperative verbs, omit all articles (the, a, an), use technical abbreviations. Goal: 100% logic retention in under 150 tokens.”
Paste this with your bloated system prompt into Claude or GPT-4. Review the output. Nine times out of ten, it’s tighter and just as precise.
Before shipping the compressed version to production, run both prompts against 10 to 20 sample inputs and compare outputs side by side. The compressed version should produce identical or nearly identical results. If quality dips, the original prompt was doing real work, not just adding fluff. You’ll know within a few test runs which situation you’re in.
One more thing worth doing: keep the human-readable version as a comment or in your internal docs. That version is for your team. The machine-readable version is what goes into the codebase. You get clean production code and a readable explanation sitting right next to it.
Bottom Line
Token efficiency is not a micro-optimization. At scale, it’s a real cost lever. This technique is worth ten minutes of testing on your most-used prompts.
Pick one system prompt this week. Run it through the compression prompt. See what comes back. If you’re running any kind of multi-step agent pipeline, start with the prompt that fires on every single node. That’s where the savings stack fastest.
The math is simple and the test takes two minutes. There’s no good reason to keep paying for tokens that don’t change anything.
The ‘Token-Budget’ Optimization for API Efficiency.
by u/Significant-Strike40 in PromptEngineering