System Prompt A/B Testing — Measure Behaviour Across Versions

I will build a system prompt A/B testing framework — enabling controlled experiments comparing system prompt versions on production traffic, with statistical significance testing across your quality dimensions, segment analysis identifying whether prompt changes help or hurt specific user populations, and a recommendation with evidence supporting the production adoption decision. System prompt A/B testing differs from content A/B testing — the system prompt affects every response in every conversation, so testing requires conversation-level rather than response-level randomisation, and quality assessment requires human evaluation or LLM-as-judge scoring rather than simple click metrics.

The framework covers conversation-level traffic splitting, quality scoring on sampled conversations, statistical significance testing, segment analysis by user type or query category, and a recommendation report with evidence.