Just fyi, that’s not entirely true. If we’re just focusing on LLMs, structured and guided generation exists. Combine that with an eval set (= unit tests), you can at least track how well you’re doing. For sure, prompt engineering misses the feeling of being in control. You’ll also never be able to claim 100% coverage (although even with unit tests that’s not something you can claim, as there are always blind spots). What you gain over traditional coding, however, is that you can tackle problems that might otherwise take an infinite number of years to express in code. For example, how would you define the rules for detecting whether an image shows a bird?
It’s just a tool like any other. Overuse is currently detestably rife. But its value is there.
Source: ML engineer who secretly hates a lot about ML but is also in awe at the developments of the last few years.
I agree with you. I just wanted to share some nuance. The point I wanted to make is that it is in fact possible to incorporate LLMs in a fairly controlled way while calculating (estimates of) the risk of failure as well as the associated social and financial costs. I do it every day, but I’m no tech bro and dislike the ‘AI will fix everything’ types as much as everyone here.