A/B Testing Prompts in Production — Yes, That’s a Thing Too
In AI CareerBot, we built infrastructure for A/B testing system prompts: we assign different versions to users, compare answer quality, and track usefulness metrics.
In AI CareerBot, we built infrastructure for A/B testing system prompts: we assign different versions to users, compare answer quality, and track usefulness metrics. In practice, the prompt is now a product hypothesis too, not just “well, this one sounds smart.”
Then the classic IT plot kicked in: a viral launch, traffic shot up, and so did API costs. To avoid a billing-induced heart attack, we added budgeting, limits, and caching for common questions.
We also covered the risks above the line: output content moderation and fallbacks if the AI responds poorly or unsafely. In 2025, when everyone is optimizing their LLM stack, you can’t really do without that anymore.
The moral is simple: in production, the winner isn’t the most “magical” prompt, but the one that delivers stable quality and doesn’t burn through the budget 😅