While large language models (LLMs) have made many routine tasks easier, they have made hiring much harder. How can we discern the authentic candidates from the AI-generated ones?

Picture this: two users need advice on a health issue. One employs an AI text interface, resulting in a plan that leaves them feeling informed and empowered. A voice interface leads the other through a back-and-forth conversation with the AI; they feel cared for, supported. Same need, two very different experiences. All because of the interface.

The general nature of LLMs makes them inherently powerful but notoriously difficult to control. Operators must defend against unintended and potentially risky interactions. Our team has investigated many of the relatively nascent solutions out there for this issue; we share what we’ve learned in this post.

Think back to your last telehealth visit with a doctor. Perhaps your kid had a persistently high fever, or you had worrying chest pain. Are you sure you were interacting with a human? What makes you sure?

Research (and especially applied research of the type that Hop does) is a type of multi-armed bandit problem — one that tries to balance new approaches (exploration) with successful approaches (exploitation). The code quality/technical debt conversation becomes a bit easier to think about if you articulate where on the exploration/exploitation spectrum you currently are.