>In-context learning solves this problem with a clever trick: instead of sending all the documents with each LLM prompt, it sends only a handful of the most rel

>In-context learning solves this problem with a clever trick: instead of sending all the documents with each LLM prompt, it sends only a handful of the most relevant documents. I don't even think this is a correct definition for "in-context learning". In-context learning is a type of few-shot learning in which examples of input/output pairs are provided as part of the prompt. The idea is that the model is able to "learn" the pattern of the task from the examples. Quoting from the GPT-3 paper: >what we call “in-context learning”, using the text input of a pretrained language model as a form of task specification: the model is conditioned on a natural language instruction and/or a few demonstrations of the task and is then expected to complete further instances of the task simply by predicting what comes next. I really don't think it's standard to refer to the process of embedding-based retrieval as "in-context learning".