Now that we have the feature deployed (behind a feature flag), and have an initial "does it do anything?" test going out today, along with an upcoming integration with our satisfaction metrics, we need to come up with how will will try to further move the needle forward.

• Run A/B test for a feature that:
• Uses a library to detect the language of a user's search query.
• Adjusts results to match that language.
• Determine from A/B test results whether this feature is fit to push to production, with the aim to:
• Improve search user satisfaction by 10% (from 15% to 16.5%).
• Reduce zero results rate for non-automata search queries by 10%.

We brainstormed a number of possibilities here:

https://etherpad.wikimedia.org/p/LanguageSupportBrainstorming

We now need to decide which of these ideas we should prioritize. We might want to take into consideration which of these can be pre-tested with our relevancy lab work, such that we can prefer to work on things we think will move the needle the most. I'm really not sure which of these to push forward on, so let us know which you think can have the most impact, or where the expected impact could be measured with relevancy lab with minimal work.