The How should you adopt LLMs? strategy explores how Theoretical Ride Sharing might adopt LLMs. It builds on several models, the first is about LLMs i

Modeling driving onboarding.

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2024-10-20 08:00:02

The How should you adopt LLMs? strategy explores how Theoretical Ride Sharing might adopt LLMs. It builds on several models, the first is about LLMs impact on Developer Experience. The second model, documented here, looks at whether LLMs might improve a core product and business problem: maximizing active drivers on their ridesharing platform.

This is an exploratory, draft chapter for a book on engineering strategy that I’m brainstorming in #eng-strategy-book. As such, some of the links go to other draft chapters, both published drafts and very early, unpublished drafts.

An obvious assumption is making driver onboarding faster would increase the long-term number of drivers in a market. However, this model show that even doubling the rate that we qualify applicant drivers as eligible has little impact on active drivers over time.

Conversely, it’s clear that efforts to reengage departed drivers has a significant impact on active drivers. We believe that there are potential LLM applications that could encourage departed drivers to return to active driving, for example mapping their rationale for departing against our recent product changes and driver retention promotions could generate high quality, personalized emails.