Pruning RAG context down to what the answer actually needs

Am I wrong to be somewhat peeved by the use of "RAG" in these contexts? I always read things like this, and wonder if instead the author should be saying "Semantic Retrieval" or something something Vector, etc. Retrieval augmented generation captures tool-use, and; semantic search of course is really just a tool under the hood.

To make an anology, in my mind, this is akin to saying "fuel air mixture system" when referring to direct fuel injection specifically, when of course, a carburetor also lives in that category.

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tl,dr: They used a rubric to have the LLM grade the chunks on a Likert scale. I think this is a good way to coax numbers out of an LLM.

Cliche topic - from a few years ago (the "RAG is dead" vs "All You Need Is Advanced RAG" BS - it came in waves and cycles, spread by bots on social media networks).

"Pruning RAG Context" is trying to recycle the old stuff (again), presuming the reader is naive (implies kapa.ai is not going anywhere). The current cycles were "openclaw" (I think that died), now we are on "harnesses" - when that dies the paid social media bots will give you something else. Shell game.

Just declare / define dictionary as a variable in your prompt to carry forward (when you decide to continue using LLMs for certain things). Also either summarize or truncate history. 3-4 year old concept. Not a big thing.