Prompt regression testing that stops surprises in production
A small prompt edit or a model upgrade can quietly change behavior. We put versioned evals in CI, so every change is scored before it ships.
The whole story, short
The problem
A prompt tweak fixes one case and silently breaks others.
What we did
- Version the eval datasets
- Add prompt regression testing to CI
- Set thresholds that block releases
- Compare old and new models
- Trace failures back to changes
The outcome
Every prompt change scored in CI
What goes wrong
- A prompt tweak fixes one case and silently breaks others.
- Model upgrades take weeks of manual checking.
- Output format changes break downstream parsers.
- Nobody can trace a quality drop back to the change that caused it.
Why the usual fix falls short
Manual spot checks do not scale with frequent prompt changes. Without thresholds agreed in advance, every release becomes a judgment call.
How we tested it
Version the eval datasets
Build test cases from production usage and past bugs, and store them in Git alongside the prompts.
Add prompt regression testing to CI
Trigger an evaluation on any pull request that touches a prompt, with a fast suite on each change and a full suite nightly.
Set thresholds that block releases
Agree per-feature score thresholds with product owners, plus a must-pass group for critical behaviors.
Compare old and new models
Run both models on the same dataset and review every case that changed.
Trace failures back to changes
Use traces to show which prompt, model setting and input caused a drop.
Questions buyers ask
What is prompt regression testing?
Prompt regression testing re-runs a fixed set of evaluation cases whenever a prompt, model, or related setting changes. It compares the new scores with a baseline, showing whether the change damaged behavior that used to work. This gives teams evidence before release, rather than complaints from users afterward.
How do you run LLM evaluations in CI/CD?
Keep evaluation datasets under version control, then trigger an evaluation run when prompts or model settings change. Score the results and compare them with agreed thresholds. If scores fall too far, the pipeline blocks the change. Keep the suite small enough to finish quickly and run larger suites less often.
How do you safely upgrade an LLM model?
Run the current and candidate models against the same evaluation dataset, then compare accuracy, safety, latency, and cost. Review the cases where results differ, not just the averages. Adjust prompts where needed, and roll out gradually so you can return to the earlier model if real usage reveals problems.