> For the complete documentation index, see [llms.txt](https://sds-bit-mesra.gitbook.io/llm-course/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://sds-bit-mesra.gitbook.io/llm-course/rag-and-llm-architecture/basics-of-rag/llm-architecture-components-for-in-context-learning.md).

# LLM Architecture Components for In-context Learning

In this brief video, Anup provides a high-level breakdown of how in-context learning operates within an LLM. He'll guide you through the journey of a prompt as it interacts with a vector database or index, undergoes similarity search, feeds context, and eventually results in a coherent LLM output.

{% embed url="<https://www.youtube.com/watch?v=OXZQBXBvOR4&start=595&end=704>" %}

Understanding this architecture is essential for mastering the interaction between prompts and LLMs, a crucial skill for anyone looking to effectively deploy these models in a variety of settings.

As you may have noticed, the concepts explained under 'in-context learning' in the forthcoming sections are essentially what RAG accomplishes. In-context learning allows your LLM to adapt and respond based on not just the pre-trained data, but also from the external, real-time information it retrieves. This is precisely what RAG is designed to do.