Picture this: you’re firing the same prompt at an API a thousand times a day. A third of it is throat-clearing your model doesn’t even need. That’s the itch a developer going by u/Key-Consequence-7530 scratched this week with PromptPocket, a fresh tool built to shrink prompts. The goal: cut the fat without losing what they’re actually asking for.
The pitch is simple. Feed it a bloated prompt, and it strips the filler, the repeated instructions, the “please make sure to” padding, while keeping the original intent intact. Then it shows you the token count before and after, side by side, so you’re not just trusting a black box. You’re watching the actual diff.
What’s new here
Most advice on cutting prompt costs boils down to “just write shorter prompts.” True, and also useless when you’re staring down a 40-line system prompt you inherited from someone who left the company. PromptPocket automates that cleanup pass and hands you a receipt. Here’s what you had, here’s what you have now, here’s the token savings in plain numbers.
For anyone running AI features at any real scale, that receipt matters more than the trimming itself. Token costs compound fast once you’re making thousands of calls a day, and “unnecessary context” is invisible right up until the invoice lands. Posted quietly in r/PromptEngineering with zero upvotes so far, this one flew under the radar. But the problem it’s chasing is one most builders already feel in their wallets.
Key features, based on what the creator shared:
- Before-and-after token count comparison, so the savings are visible, not assumed
- Automatic trimming of repetitive or unnecessary wording
- A stated goal of preserving the original intent of the prompt, not just shortening it blindly
- A live web demo you can test with your own prompts right now
The twist
Here’s the part I didn’t expect: the tool isn’t just counting words and calling it done. It’s built around the idea that the trim has to preserve intent, and that’s the actual hard problem. Anyone can chop a prompt in half. Keeping the model’s output identical after you’ve done it is where most trimming attempts quietly fall apart.
The creator is upfront that this is early. They’re still exploring how to make PromptPocket more useful for developers and AI builders. They’re asking the community directly what techniques people already use to cut tokens. That’s a live, evolving build, not a finished product, which means the door’s wide open for feature requests right now.
How to try it in five minutes
- Grab a prompt you use often, ideally one you haven’t touched since you first wrote it. Those are usually the bloated ones. 📝
- Run it through PromptPocket and check the before-and-after token count. 🔍
- Test the trimmed version against the same inputs you’d normally send it. ▶️
- Compare outputs line by line. If tone shifted or a constraint got dropped, the trim went too far. ⚖️
- Keep whichever version matches your original output at the lowest token count, then swap it into your workflow.
Pro tip
Don’t hand any optimizer, this one included, your highest-stakes prompt on the first pass. Run the before-and-after comparison across five or six real inputs, not just one, before it goes anywhere near production. A trimmed prompt can look perfect on your test case and still quietly drop a constraint that only shows up on an edge case.
Second tip: if you already track API spend per feature, tag which prompts went through a trimming pass. Six months from now you’ll want proof the savings held. Prompts get patched and re-patched, and savings quietly creep back up.
Where it fits
This isn’t the first swing anyone’s taken at prompt compression. Plenty of teams hand-trim prompts or run their own regex cleanup before shipping. There are heavier compression frameworks out there too, for people optimizing at serious scale. What PromptPocket adds is a simple, visible before-and-after in one place, so you’re not guessing whether the edit actually helped or just feels shorter. That visibility alone is worth something. Most developers never check whether their “optimized” prompt still does the job. They just assume it does because the token count went down.
Worth noting: this is a zero-upvote post from a builder still shaping the roadmap, not a polished, battle-tested product with a track record. Treat the output the way you’d treat any early tool: verify it, don’t just trust the label.
If you’ve got a prompt sitting untouched in your codebase since March, quietly costing you money every call, this is worth five minutes. Go run your worst offender through it and see what comes back 🚀
I built a tool to optimize prompts and reduce unnecessary token usage — feedback wanted
by u/Key-Consequence-7530 in PromptEngineering