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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.




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