Comparison
A/B testingvsShadow deployment
A/B testing
you serve the new prompt to half the traffic and the old one to the other half, and let the outcome metric decide instead of your opinion.
Serving variants (prompts, models, pipelines) to split traffic and comparing outcome metrics. The same discipline as any product experiment, applied to probabilistic components; your existing statistics instincts transfer unchanged.
Full entry →Shadow deployment
you run the candidate on live traffic without showing anyone its answers, and compare the logs afterwards.
Running a candidate (new model, new prompt) on live traffic without serving its output, logging results for offline comparison against the incumbent. The lowest-risk way to test a change against reality before it faces a user.
Full entry →