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.

What is Retrieval Augmented Generation (RAG)?

Michelle (Gress) Rideout on LinkedIn: Creating High Quality RAG Applications with Databricks

Marcelo Sales on LinkedIn: Enhancing your team's performance by building a data culture

Improve your RAG application response quality with real-time structured data

Databricks Clusters 101 - A Comprehensive Guide to Create Clusters (2024)

Why the AI Hyperrealists at Databricks Spent $10 Million to Beat Meta's LLM — The Information

Zachary Sansone, CFA on LinkedIn: S&P Global uses Databricks Lakehouse to process more than 700 billion data…

Databricks on X: Evaluate and tune the performance of your RAG applications with #MLflow 2.8 LLM-as-a-judge! Find out how it can compare and contrast #LLMs to navigate through your RAG application requirements👇

Renan Valente on LinkedIn: Real-Time, Data-Driven Decision-Making with Databricks - Koantek

Chiara Fumagalli on LinkedIn: #lakehouse

Yvette Ramirez on LinkedIn: #innovation #technology #data #machinelearning #datascience #wearehiring

Retrieval Augmented Generation (RAG) on Databricks

Databricks Launches Data Intelligence Platform