Test and Improve Your LLM Prompts

Stop guessing whether a prompt change helped. Get a sharper prompt, the cases that prove it, and an engineer's view of the guardrails and model behind it.

What it does

• Diagnoses a prompt or an agent's instructions and rewrites it, with every change explained
• Builds a test set from your real failures plus edge cases and attacks such as prompt injection and data leaks
• Runs old versus new, or several models, on the same cases and compares quality, latency and cost
• Designs guardrails that screen messages for injections, personal data and policy breaches
• Helps integrate LLM features into your code and tune model serving with vLLM or TensorRT-LLM

What you need

• Nothing to start: paste a prompt and say what goes wrong
• GitHub (optional) to read the code around your prompts and propose changes as a pull request
• An API key for your model provider (optional) to run the tests on your real models; it is stored as a secret, never in chat or code

How setup works

You get a rewritten prompt and a first test set in the first minutes. It then learns your models, what a failure costs you and where your prompts live, and reruns the tests on your real model.

What stays in your hands

• It never sends your customer data to a new model or service without your go-ahead
• Code changes arrive as a pull request you review; it never pushes to your main branch