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Implement Retrieval Augmented Generation (RAG) with Azure Databricks - Training
Implement Retrieval Augmented Generation (RAG) with Azure Databricks
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This article describes how retrieval-augmented generation lets LLMs treat your data sources as knowledge without having to train.
LLMs have extensive knowledge bases through training. For most scenarios, you can select an LLM that is designed for your requirements, but those LLMs still require additional training to understand your specific data. Retrieval-augmented generation lets you make your data available to LLMs without training them on it first.
To perform retrieval-augmented generation, you create embeddings for your data along with common questions about it. You can do this on the fly or you can create and store the embeddings by using a vector database solution.
When a user asks a question, the LLM uses your embeddings to compare the user's question to your data and find the most relevant context. This context and the user's question then go to the LLM in a prompt, and the LLM provides a response based on your data.
To perform RAG, you must process each data source that you want to use for retrievals. The basic process is as follows:
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Training
Module
Implement Retrieval Augmented Generation (RAG) with Azure Databricks - Training
Implement Retrieval Augmented Generation (RAG) with Azure Databricks