---
title: "Contextual RAG"
sidebarTitle: "Contextual RAG"
description: "Prepend LLM-generated context to each chunk before embedding it."
---

Addresses the "lost in the middle" problem in retrieval. An LLM generates a
succinct piece of context for each document chunk, which is prepended to the
chunk before embedding -- so a chunk carries enough surrounding meaning to be
retrieved for the right queries.

<Note>
This example is a runnable notebook in the
[vectordb-recipes](https://github.com/lancedb/vectordb-recipes) repository rather than a page here.
</Note>

<CardGroup cols={2}>
  <Card title="Open in Colab" icon="google" href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Contextual-RAG/Anthropic_Contextual_RAG.ipynb">
    Run it end to end in the browser.
  </Card>
  <Card title="View on GitHub" icon="github" href="https://github.com/lancedb/vectordb-recipes/tree/main/examples/Contextual-RAG">
    Read the source.
  </Card>
</CardGroup>