> ## Documentation Index
> Fetch the complete documentation index at: https://docs.ai21.com/llms.txt
> Use this file to discover all available pages before exploring further.

# RAG Overview

Built on top of AI21’s advanced RAG Engine and enhanced with a planning layer, AI21 Maestro enables users to ask follow-up questions and receive grounded, context-aware answers.\
You can extend AI21 Maestro capabilities using built-in tools that provide access to additional context and information from the web or your files.

* **File Search:** Retrieve information from your uploaded documents.
* **Web Search:** Incorporate data from the web.

## Key Benefits

**Chat with Your Data**\
Go beyond single-question answering by allowing follow-up questions, clarifications, and step-by-step problem-solving through a natural conversation.

**Enterprise-Grade Accuracy**\
Built on retrieval-augmented generation (RAG), responses are grounded in your actual documents, not just model hallucination. It’s useful for internal tools and customer-facing applications alike.

**Fully Managed & Easy to Deploy**\
Simply upload your documents (PDF, DOCX, TXT, HTML, or Markdown). The RAG Engine automatically indexes them, making setup fast and seamless.

## **Deployment & Document Ingestion**

* Upload supported document types: .pdf, .docx, .txt, .md, .html.
* Indexing is automatic.
* The AI21 Maestro RAG system includes our in-house document parser, which provides high-quality parsing.
* Data connectors to cloud sources.