PromptProof brings statistical rigor to prompt engineering. Confidence intervals tell you if gains are real, not noise. Test images, videos, and PDFs on major LLMs in one place. Shared datasets, ground truth labels, and role-based access keep your team aligned. In production, trace real user inputs and let team members label them to validate accuracy. Built by an ML engineer tired of shipping "improved" prompts they couldn't prove with other LLMOps tools - not whether you're actually improving.

Customer.io — Automate Messaging Everywhere — Startups Get 12 Months Free

Automate Messaging Everywhere — Startups Get 12 Months Free

Hi, all! 👋 I built PromptProof out of a real pain I hit while developing a PDF extraction feature powered by LLMs. The prompt worked perfectly on the sample documents I was given. But once real user data came in, edge cases emerged. When I fixed those, the prompts didn't work well on the sample documents. It became a game of whack-a-mole with no way to measure if I was actually moving forward. Then came the real challenges: supporting new document formats without regressions, improving latency, upgrading LLM models — and each time, I had no reliable way to verify that the extracted data was still correct, not just that the API call succeeded. I realized this isn't unique to my project. Every team building LLM-powered features lives with this uncertainty. That's why I built PromptProof — to bring the kind of statistical confidence to prompt engineering that software engineers already expect from their test suites. Would love to hear from anyone who's faced the same pain. What's your biggest challenge when iterating on prompts in production? 🙏