# In-Context Learning ### What is In-Context Learning? In-Context Learning (for Building AI Co-Pilots) using the Retrieval Augmented Generation (RAG) pattern enhances large language models (LLMs) by integrating them with external information retrieval systems. \ Rather than relying exclusively on an LLM's pre-trained knowledge, RAG taps into external sources to provide contextually rich responses. This is crucial since the knowledge within LLMs is confined to a specific cutoff date. Building AI Co-Pilots utilizing this approach demands a considerable amount of technical expertise, given the requirements such as creating embeddings, devising chunking strategies, and establishing a vector database.\ \ [Please refer to this mindmap to understand how the responses of foundational models are altered based on the chosen customization technique.](/overview.md) --- # 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://docs.texti.ai/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.