Models have improved a lot over the last couple of years. Building something impressive with one is also getting easier. I’m less convinced that the engineering around those models has moved at the same pace. You can put a much stronger model into a system and still run into problems with context, retrieval, tool use, evaluation, monitoring, or just figuring out why a particular run went wrong. I’ve run into cases where improving the model made the system noticeably better, but didn’t really make the underlying engineering problems disappear. It makes me wonder how much of the work ahead is going to be about improving the models versus getting much better at building reliable systems around them. Where do you think the bigger gap is right now? submitted by /u/Meher_Nolan
Originally posted by u/Meher_Nolan on r/ArtificialInteligence
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