From COBOL in the 1960s to AI in the 2020s, every generation promises to eliminate programmers. Explore the recurring cycles of software simplification hype.
Rag was never supposed to be about learning over time. It was supposed to provide better context at inference. It could never scale to handle new learning beyond focused concepts.
The way it was presented with regards to search engines was that it was supposed to pull data that was more up-to-date than when the model was trained. It does do that, actually, and provides better results too, on average anyway.
But that’s just one domain, and “better” doesn’t mean “good” or “accurate”. In most domains, at least where I work, we’ve found that RAG overcomplicates things for little benefit, unfortunately.
Rag was never supposed to be about learning over time. It was supposed to provide better context at inference. It could never scale to handle new learning beyond focused concepts.
The way it was presented with regards to search engines was that it was supposed to pull data that was more up-to-date than when the model was trained. It does do that, actually, and provides better results too, on average anyway.
But that’s just one domain, and “better” doesn’t mean “good” or “accurate”. In most domains, at least where I work, we’ve found that RAG overcomplicates things for little benefit, unfortunately.