Multi-model refinement engine
AI-powered prompt optimization across Claude, GPT, and Gemini. Brain dump a rough idea, answer a few questions, get a precision prompt. The engine stress-tests and sharpens — not just rewrites.
Case study — 01
The definitive prompt-engineering workspace. Rough idea in, precision prompt out — across Claude, GPT, and Gemini.
Whetstone came to Kleta with a clear vision: build the definitive workspace for prompt engineering. Teams were drowning in scattered prompt docs, losing track of what worked, and wasting hours on manual iteration.
We designed and shipped a complete platform — from AI-powered refinement to folder-based organization — in eight focused weeks. The result is a tool that turns rough ideas into precision prompts.
AI-powered prompt optimization across Claude, GPT, and Gemini. Brain dump a rough idea, answer a few questions, get a precision prompt. The engine stress-tests and sharpens — not just rewrites.
A bold, typographic design system built for focus. Every pixel tuned for long prompt-writing sessions with zero visual fatigue. Dark-first because prompt engineers live in terminals.
Folders, tags, and instant search. Organize hundreds of prompts with a system that scales from solo users to engineering teams. Never lose a working prompt again.
Copy, reuse, and iterate on your best prompts instantly. Shared workspaces with version history so teams can build on each other's best work.
We started with the core loop: write a rough prompt, refine it with AI, save it for reuse. Everything else grew from that single interaction.
The refinement engine queries multiple models to stress-test and sharpen prompts — not just rewrite them. We built a three-step flow: Brain Dump your rough idea, Smart Refine with guided questions, then arrive at a Gold Standard prompt.
The interface was designed dark-first because prompt engineers live in terminals and code editors. We matched that energy with a system that feels native to their workflow — fast, keyboard-friendly, and visually quiet.
Next step
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