Building High Quality RAG Applications with Databricks

Retrieval-Augmented-Generation (RAG) has quickly emerged as the canonical way to incorporate proprietary, real-time data into Large Language Model (LLM) applications. Today we are excited to announce a suite of RAG tools to help Databricks users build high-quality, production LLM apps using their enterprise data.

Large Language Models (LLMs) for Retail

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Retrieval Augmented Generation (RAG)

Building and Customizing GenAI with Databricks: LLMs and Beyond

Part1: Implementing a RAG chatbot with Vector Search, BGE, langchain and llama2 on Databricks

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Renan Valente on LinkedIn: Implementing Disaster Recovery for a Databricks Workspace

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Build Retrieval-Augmented Generation (RAG) with Databricks and Pinecone

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Chris Anello on LinkedIn: Creating High Quality RAG Applications with Databricks