Editorial recipe โ not benchmarked โ reviewed September 3, 2026
Catch an AI workflow regression before release
For teams changing prompts, models, tools, or agent instructions against repeatable examples.
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Procedure
- Freeze representative success, edge, and refusal cases with expected deterministic properties; keep restricted production data out of the fixture.
- Pin the prompt, provider identifiers, model settings, tools, and budget in one Promptfoo configuration and prefer deterministic assertions before model grading.
- Run the baseline and candidate on the same cases with cache behavior recorded; export results, latency, token use, cost, and failures as versioned artifacts.
- Run the configuration in isolated CI with scoped provider credentials and block only on reviewed assertions that represent real product requirements.
- Have a human inspect failures and a sample of passes, record accepted tradeoffs, and add real escaped failures to the regression set without storing customer secrets.
Acceptance artifact
A pinned evaluation configuration, frozen cases, comparable run exports, CI result, and reviewed regression decision
Do not use it blindly
Promptfoo configurations can execute code and are not a sandbox; treat downloaded configurations and fixtures as untrusted and isolate them from production secrets.