# Enterprise RAG

Use Spice to access data across various data sources for Retrieval-Augmented-Generation (RAG).

Spice enables developers to combine structured data via SQL queries and unstructured data through built-in vector similarity search. This combined data can then be fed to large language models (LLMs) through a native AI gateway, enhancing the models' ability to generate accurate and contextually relevant responses.


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.spice.ai/use-cases/enterprise-rag.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.