I think the acronyms and names for things are in constant flux and might not be agreed upon in any large subset of people working in the field. For example I've built a system employing what the author calls "guardrails" and I've never heard of the term. And obviously I've been using RAG, but I've been calling it in-context learning, never saw the need to emphasise that the context is retrieved from somewhere, seems a bit obvious. And I've been looking at how AutoGPT works for inspiration and they call their evals "challenges" so that's how I approached that problem.

RAG and in context learning are not the same thing

https://www.hopsworks.ai/dictionary/in-context-learning-icl

https://www.hopsworks.ai/dictionary/retrieval-augmented-llm

How do you figure? In the ICL article one of their examples ("How to Engineer Prompts for In-Context Learning") literally describes RAG.

And neither of the articles even mentions the other.

In context learning is a superset of RAG, the theoretical underpinning for why RAG works. Retrieval is not necessary for ICL.

Tbf, if you work as a research scientist - a solid part of your work is to read new papers every single day. The ML/AI field is not some unified field, so you will se A LOT of papers from all kinds of researchers and scientists, from all kinds of fields in the world of science.

Sometimes the terms and metrics are borrowed from the (paper) authors domain, other times they're just coined on the fly - if there are no good analogies, or there are too many overlapping or closely related terms. It's like in math where you have a double-digit number of notations for the dot product - all depending on what sub-field of math you're working on.

I understand WHY it is that way, but it's super frustrating - because you end up spending time to look up what the notation / terms mean, often with no real clear answer.

I just skimmed the article, but to me it just felt like they were using new terms for concepts we've already had for a long time in regular software engineering.