This feature facilitates A/B testing on targeted groups of users to evaluate how different configurations of language models impact real users in production settings. By systematically varying features, parameters, or entire setups of the language models, and observing how different user segments respond, you can gather concrete data on user behavior and preferences.

This strategic testing allows for precise refinements to the models, ensuring that they are optimized to meet the diverse demands of the user base, ultimately enhancing user experience and model effectiveness in real-world applications.

Examples of target audiences for A/B testing could include:
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• New users versus returning users
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• Users from different geographic regions

• Users with varying levels of technical expertise
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• Users talking about different topics