# RAG-Powered E-Commerce Chatbot

In this tutorial, we're going to create a very simple e-commerce chatbot that uses [retrieval augmented generation](/guides/text-generation-retrieval-augmented-generation-rag) to answer questions. On their own, large language models can often produce remarkably good natural language, including poems, explanations of tricky scientific concepts, and convincingly-human dialogue.

But they are also well-known to hallucinate factually incorrect, nonsensical, or incomplete information in their replies, which can be problematic for certain use cases.

RAG substantially reduces this problem by giving the model source material to work with. Rather than simply generating an output based on the input prompt, the model can pull information out of this material and incorporate it into its reply.

As things stand, this source material can come from one of two places:

- The user can directly provide context-rich documents to ground replies.
- The user can specify the location of the documents (this mode operates via ‘connectors’, which we will cover in more detail below).

We'll focus on document mode for this demonstration, but you can read about connectors and the rest of RAG in the [appropriate documentation](/guides/text-generation-retrieval-augmented-generation-rag).

## Setup

First, let's install the SDK:

## Related pages

- [Changelog](../changelog.md)
- [Cohere](../index.md)
- [Cohere API](./cohere-api-index.md)
- [Cohere Labs](./cohere-labs-index.md)
- [Cohere Platform](./cohere-platform-index.md)
- [Cookbooks](./cookbooks-index.md)
- [Deployment Options](./deployment-options-index.md)
- [Embeddings (Vectors, Search, Retrieval)](./embeddings-vectors-search-retrieval-index.md)
- [Get Started](./get-started-index.md)
- [Going to Production](./going-to-production-index.md)

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