This analogy disregards all the work that happens behind the scenes to make modern Python workload work. The current landscape is moving so fast that we don't

This analogy disregards all the work that happens behind the scenes to make modern Python workload work. The current landscape is moving so fast that we don't stop and work on achieving the best version of a specific approach. One can argue that both RAG and having 1M+ context length is a symptom of a disconnect between what language models are capable of and what people think they can achieve with them. Within the next 6 months there might be yet another approach to provide LLMs custom memory, and we will be having the same discussion the way things are handled currently.