Comment by pussyjuice

After a couple years of multi-modal LLM proving out product, I now consider RAG to be essentially "AI Lite", or just AI-inspired vector search.

It isn't really "AI" in the way ongoing LLM conversations are. The context is effectively controlled by deterministic information, and as LLMs continue improve through various context-related techniques like re-prompting, running multiple models, etc. that deterministic "re-basing" of context will stifle the output.

So I say over time it will be treated as less and less "AI" and more "AI adjacent".

The significance is that right now RAG is largely considered to be an "AI pipeline strategy" in its own right compared others that involve pure context engineering.

But when the context size of LLMs grows much larger (with integrity), when it can, say, accurately hold thousands and thousands of lines of code in context with accuracy, without having to use RAG to search and find, it will be doing a lot more for us. We will get the agentic automation they are promising and not delivering (due to this current limitation).