What teams use LLMJury for

Five jobs, one product. Most teams arrive for one of them — usually versioning, because it pays off before you have run a single experiment — and end up using the rest as the same loop applies to more of what they ship.

• Prompt management and versioning

  Move your prompts out of string literals and into one place with full history, an audit trail, and one-click rollback — useful on day one, before you test anything.

  Teams whose prompts live in string literals across the repo, where changing a comma means a redeploy and the version history is `git blame`.

• A/B testing prompts on live traffic

  Run two or more rewrites against each other on real users and get a graded verdict — which one wins, by how much, and whether the number means anything.

  Anyone who has picked between two prompts by reading ten outputs and going with the one that felt better.

• Comparing models on your own traffic

  Route a slice of real traffic to the new model and read quality, speed, and cost side by side — before you commit the whole product to it.

  Teams facing a model upgrade, a cheaper alternative, or a provider migration, with no way to know what it does to their product until it is live.

• Tuning parameters and configuration

  Temperature, max tokens, retrieval settings, chunk size — test the whole permutation behind one variant key instead of arguing about defaults.

  Teams whose retrieval and generation settings were chosen once, early, by whoever was there — and have never been measured since.

• 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.

  Teams whose LLM feature is now load-bearing, and who have discovered that nothing in their monitoring goes off when it gets worse.