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.

How Providence Health Built a Model marketplace using Databricks

Dastan A. on LinkedIn: Introducing Databricks Vector Search Public Preview

Generative AI Architecture Patterns

How Snorkel Flow users can register custom models to Databricks

Community How to build RAG Applications that Reduce Hallucinations

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

Community How to build RAG Applications that Reduce Hallucinations

Živilė Norkūnaitė on LinkedIn: Home - Data + AI Summit 2022

Conor B. Murphy on LinkedIn: Sr. Data Scientist/Machine Learning Engineer

Best Practices for LLM Evaluation of RAG Applications

Retrieval Augmented Generation: Everything You Need to Know - Vectara

Hylke Visser on LinkedIn: Enhancing RAG-based application accuracy by constructing and leveraging…

Building and Customizing GenAI with Databricks: LLMs and Beyond

Galit Raz on LinkedIn: Databricks Announces Lakehouse Apps and Expands the Databricks Marketplace…