The general nature of LLMs makes them inherently powerful but notoriously difficult to control. When building an LLM-based product or interface that is exposed to users, a key challenge is limiting the scope of interaction to your business domain and intended use case. This remains an “unsolved” problem in practice mostly because modern LLMs are still susceptible to disregarding instructions and hallucinating (i.e., factual inaccuracy). As a consequence, operators must defend against unintended and potentially risky interactions. That can be difficult, because the ecosystem and tools for this problem are relatively nascent. Few (if any) commercial or open-source software packages offer out-of-the-box solutions that are accurate, simple, and affordable. We know, because our team has investigated many of these solutions, including AWS Bedrock Guardrails, NVIDIA NeMO Guardrails, and others.
Read moreCould You Be Talking to an AI Doctor?
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? Perhaps the doctor listened attentively to your symptoms, asked pertinent questions, and even picked up on subtle cues in your language that hinted at the severity of your condition.
Read moreCode Quality for Research
I view research (and especially applied research of the type that Hop does) as 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 is usually a bit muddled these days, but it becomes a bit easier to think about if you articulate where on the exploration/exploitation spectrum you currently are.
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