Take your longest system prompt. Count the words.
500? 800? More? That’s real money you’re spending every single time it runs. Every API call, every pipeline execution, every automated task your agent handles. Those tokens add up fast, and most founders and builders have no idea how much of that cost is pure filler.
Here’s the challenge: compress it down to 10% of its original size without breaking a single constraint.
Sounds impossible. It’s not. The technique is called Semantic Compression, and once you try it, you’ll never look at a bloated prompt the same way. Think of it as the difference between carrying a full suitcase on a day trip versus packing only what you actually need. The destination is the same. The luggage is not.
🧠 The Concept: Logic Seeds
A Logic Seed is a compressed version of your instructions that keeps 100% of the functional constraints at roughly 10% of the word count.
Not a summary. A distillation. Machine-shorthand that the AI reads exactly the same way it reads the full version. The difference is that summaries preserve meaning for humans. Logic Seeds preserve behavior for models. Those are two very different goals, and conflating them is where most people go wrong when they try to shorten prompts on their own.
Why does this work? Because language models don’t need the same scaffolding that humans do. You write “please make sure to always respond in a professional tone and avoid using casual language or slang in any of your responses” and the model gets it from “tone: formal, no slang.” Same output. One seventh of the tokens. The full version was written for a human reader. The seed is written for the machine.
Most bloated prompts are full of explanations that justify the rule rather than state it. Logic Seeds strip the justification and keep only the constraint. The model doesn’t need to understand why. It just needs to know what.
📋 How to Do It
- Open your most complex system prompt
- Count the words (500 words is a perfect test subject)
- Paste it with this exact prompt: “Rewrite these 500 words of instructions into a Logic Seed of 50 words that retains 100% of the functional constraints.”
- Run the Logic Seed on 3 to 5 real inputs that you’ve already run through the original prompt
- Compare results side by side with the original, looking specifically at tone, format, edge case handling, and any rules you set around what to avoid
The side-by-side comparison is where the real learning happens. Don’t just skim the outputs. Look for places where the seed produces a slightly different structure, a different level of formality, or skips something the original always included. Those gaps tell you exactly which parts of your prompt were doing actual work versus which parts were just taking up space. You want that information. It makes every future prompt you write sharper from the start.
🔍 What the Results Tell You
If the Logic Seed performs identically, your original prompt was mostly filler. You’ve been paying for fluff at scale. Across thousands of runs, that filler has a real dollar cost attached to it. Knowing this is useful even if you never touch another prompt again.
If something breaks, you just found a constraint that wasn’t pulling its weight efficiently. Fix the seed. Don’t go back to the bloat. Add one precise sentence that captures the missing behavior. Something like “always end with a bullet summary” is more useful than three paragraphs explaining why summaries help readers. Keep the seed lean and targeted. The goal is a prompt that works like a key, not one that works like a map.
Sometimes you’ll find that the seed actually outperforms the original. That’s the most interesting result. It means the extra instructions were creating interference, not clarity. The model was navigating around conflicting signals buried in the length. Shorter often means cleaner reasoning, not less capable output.
💡 Extra Tips
- Works best on rule-heavy system prompts, not casual conversation starters
- Save your Logic Seeds in a library. They compound over time. A well-compressed seed from one project often transfers directly to another with minor tweaks.
- Run the test on your top 5 prompts this week and see which ones shrink cleanly
- If the seed breaks on edge cases, add one targeted sentence rather than reverting to the full version
- Label each seed with the date you compressed it and the original word count. Watching that number drop across your library is genuinely motivating.
- If you’re running agents at scale, even a 60% token reduction on a high-frequency prompt can cover a month of API costs. Do the math before you dismiss this as a small optimization.
🎯 Your challenge: Pick one prompt right now. Run the compression. See what actually survives.
The ‘Semantic Compression’ Logic Seed.
by u/Significant-Strike40 in PromptEngineering