In the field of AI application development, A/B experimentation has emerged as a critical practice. It allows for continuous evaluation of AI applications, balancing business impact, risk, and cost. While offline and online evaluations provide some insights, they need to be supplemented with A/B experimentation to ensure the use of right metrics for measuring success. A/B experimentation involves comparing two versions of a feature, prompt, or model using feature flags or dynamic configuration to determine which performs better. This method is essential for several reasons:
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