Solutions
Guardrails against quality regressions
Ship every prompt change as an experiment first, so a worse variant shows up in the dashboard instead of a support ticket.
Who this is for: Teams whose LLM feature is now load-bearing, and who have discovered that nothing in their monitoring goes off when it gets worse.
What most teams do today — and where it stops working
An LLM regression raises no alarms. The service is up, latency is fine, error rates are flat, and the answers are worse. Every signal a normal deploy is watched by stays green, because none of them look at the output.
It also does not arrive as an event. A prompt tweak, a provider-side model update, a retrieval index that drifted — each moves the average a little, and a site-wide average is exactly the thing that hides it.
So the detector ends up being a customer, weeks later, and the investigation starts with nobody able to say what the prompt said when it broke.
How LLMJury does it
Every change ships as an experiment
The new version goes to a slice, not to everyone. If it is worse, the people who see it are a fraction of your users and the evidence arrives as a verdict rather than a complaint.
Guardrail metrics you declare in advance
Mark the metrics you refuse to regress — safety, quality, latency, cost. Their role is declared before the data arrives, which is what makes "it did not hurt anything" a finding rather than an interpretation.
Distributions, not just averages
Every metric shows how each arm’s raw scores are spread. A change that helps most requests and badly hurts a few has the same mean and a visibly different histogram.
A permanent record of what shipped
The prompt version, the config, the verdict, and who approved it are all kept together. When something does go wrong, the first question — what changed, and when — has an answer.
Roll back without a deploy
Promoting the previous version is a click. The recovery path does not go through a build.
What you would set up
- 1Make the current behaviour the controlWhatever is live today is version 1 and arm 1. Nothing about your users’ experience changes when you start.
- 2Declare the guardrails onceQuality, safety, latency, cost — with the direction that counts as worse. They carry over to every experiment on that surface.
- 3Make the experiment the default way to change the promptNot just for risky edits. The cheap ones are where an unnoticed regression survives longest.
The whole path, in all three SDKs, is on the five-minute quickstart.
What this does not do
- This is a pre-release guardrail, not production alerting. LLMJury tells you a variant is worse than the control; it does not page anyone at 3am, and there is no alerting integration yet.
- A guardrail only guards what it measures. A regression in a dimension you did not declare a metric for is invisible here, same as anywhere.
- Judge-scored guardrails need the Pro plan and enough sample to be conclusive. On Free the guardrails available are latency, cost, and business outcomes.
Try it on the thing you are arguing about this week
Free plan, no credit card, and the sample experiment is already in your account so there is something to read before you have any data of your own.
Prefer to look first? The interactive demo is a real verdict with no sign-up.