I think there are 2 layers in an AI application that can be considered somewhat orthogonal: 1. Making your models network accessible in a configured way 2. RAG

I think there are 2 layers in an AI application that can be considered somewhat orthogonal: 1. Making your models network accessible in a configured way 2. RAG and any chaining code for model calls For #1, this is probably going to start with pytorch since that's typically what research code uses, and get hand optimized over time as your application matures. For #2, it seems like the most popular tools so far are in the Python space, of course that doesn't mean other languages don't have robust ecosystems for AI already and of course new things get developed all the time, but ecosystem size is important to consider.