Case studyArchitecture, governance, and how to adapt this pattern in a pilot
Business use case
Teams change prompts in production and only discover regressions after customers complain. This pattern makes “prompt change” behave like “code change”: replay, score, and block bad merges.
Solution
Run a stored golden conversation through your system prompt, generate an answer, then use a rubric to score correctness and risk.
Delivery playbookDiscovery → pilot → scale
- 1Discovery2–4 wks
Create 20 to 50 golden conversations by intent; define rubric criteria and regression thresholds.
- 2Pilot6–8 wks
Run replay evals on every prompt/tool change; block deploys on high-risk regressions.
- 3Scaleongoing
Expand golden set by channel; trend scores over time and tie to incident reports.
Where else this appliesGolden conversation replay is how you turn prompt changes into measurable quality gates. It is the cheapest way to stop silent regressions.
Prompt versioning
Block deployments when safety or grounding behaviour regresses.
Tooling changes
Replay before enabling new tools or write actions for agents.
Model swaps
Compare quality when procurement mandates a provider change.
Governance reviews
Provide evidence to risk teams beyond anecdotal demos.
Works well with AI SDK routes: run in CI, run in admin tools, or run during workshops when teams propose prompt edits.