A great article - just a technical nitpick for the author: "in-context learning" is not what you do with RAG. "In context learning" is a really confusing name for reasoning by analogy. In RAG you provide source information in the prompt - in ICL you provide examples of how the task should be accomplished. :

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In-context learning in language models, also known as few-shot learning or few-shot prompting, is a technique where the model is presented with prompts and responses as a context prior to performing a task. For example, to train a language model to generate imaginative and witty jokes.

We can leverage in-context learning by exposing the model to a dataset of joke prompts and corresponding punchlines:

    Prompt 1: “Why don’t scientists trust atoms?” Response: “Because they make up everything!
    Prompt 2: “What do you call a bear with no teeth?” Response: “A gummy bear!”
    Prompt 3: “Why did the scarecrow win an award?” Response: “Because he was outstanding in his field!”

By training in different types of jokes, the model develops an understanding of how humor works and becomes capable of creating its own clever and amusing punchlines.

"""

from https://www.techopedia.com/from-language-models-to-problem-s...