> For the complete documentation index, see [llms.txt](https://docs.ragg.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.ragg.ai/getting-started/introduction.md).

# Introduction

What is Raag and why it matters?

## What is Ragg?

**Ragg** provides a simple SDK to connect multiple internal and external data sources to Large Language Models (LLMs).

Ragg is a **retrieval augmented generation** (RAG) pipeline as a service. It allows you to spend more time coding business logic using your data than trying to get a fully functional RAG pipeline.

## Why Ragg?

Regarding RAG, developers face many new challenges for efficiently ingesting and storing data and later providing hallucination-free and context-aware LLM responses. 

Raag helps you to tackle all these challenges with a simple, fiction-free SDK.

#### Here are some challenges we let you handle with ease:

* Data ingestion
  * Website crawling on regular basis
  * Document parsing
  * Storage and indexing
* Search
  * Indexing
  * Storage
  * Reranking strategies
  * Noise elimination
* Retrieval Augmented Generation
  * Prevent hallucination
  * Prevent LLM reverse engineering or context hacking