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.
How To Create Realistic Test Data For Databricks With Tonic, Blog
Build Retrieval-Augmented Generation (RAG) with Databricks and Pinecone
Retrieval Augmented Generation (RAG)
Brandon Bishop on LinkedIn: Reducing Our Data Infrastructure Costs by 76% by Migrating from Snowflake…
Build Retrieval-Augmented Generation (RAG) with Databricks and Pinecone
Community How to build RAG Applications that Reduce Hallucinations
Part3: Implementing a RAG chatbot with Vector Search, BGE, langchain and Mistral 8x7B on Databricks
Volker Tjaden auf LinkedIn: Nasdaq uses Databricks Lakehouse for BI and ML applications with real-time…
Chiara Fumagalli on LinkedIn: #lakehouse