People need to be more thoughtful building products on top of LLMs. The fact that they generate text is not the point.

Such a thing is compelling not because it offers  answers  in the form of text, but because it makes it possible to play text—all the text, almost—like an instrument.

That outcome could be revelatory! But a huge obstacle stands in the way of achieving it: people, who don’t know what the hell to make of LLMs, ChatGPT, and all the other gene

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We generally lean towards picking more advanced commercial LLMs to quickly validate our ideas and obtain early feedback from users. Although they may be expensive, the general idea is that if problems can't be adequately solved with state-of-the-art foundational models like GPT-4, then more often than not, those problems may not be addressable usin... See more

Like all machine learning, LLMs turn a logic problem into a statistics problem: instead of people writing the pattern for each possible question by hand, which doesn’t scale, you give the machine a meaningful sample of all the text and data that there is and it works out the patterns for itself, and that does scale (or should do). You get the machi... See more