# In-Context Learning In this video, you'll be introduced to the concept of in-context learning through prompts. Anup explains how this form of learning is scalable, particularly when dealing with vast amounts of data. This becomes especially relevant when we recall our earlier discussions on Retrieval Augmented Generation (RAG). Understanding in-context learning amplifies the efficacy of technologies like RAG in Large Language Models. {% embed url="" %} --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://ai-community-iitb-organization.gitbook.io/10-days-realtime-llm-bootcamp/retrieval-augmented-generation-and-llm-architecture/in-context-learning.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.