Building High Quality RAG Applications with Databricks
4.7 (508) · $ 10.50 · In stock
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 Snorkel Flow users can register custom models to Databricks
Živilė Norkūnaitė on LinkedIn: Home - Data + AI Summit 2022
Exclusive: Databricks launches new tools for building high-quality RAG apps
A RAG approach using Databricks AI Lakehouse functionalities, by Lavinia Hriscu, SDG Group
Retrieval Augmented Generation (RAG) on Azure Databricks - Azure Databricks
Blake Karpe posted on LinkedIn
Renan Valente on LinkedIn: Real-Time, Data-Driven Decision-Making with Databricks - Koantek
Introducing Generative AI courses, Scott Ryan posted on the topic
Kasey Uhlenhuth on LinkedIn: Using MLflow AI Gateway and Llama 2 to Build Generative AI Apps
Boost the Performance of Your Databricks Jobs and Queries
Tammy Welles on LinkedIn: Best Practices for LLM Evaluation of RAG Applications
Maggie Wang - Databricks
Retrieval-Augmented Generation (RAG) Tutorial, Examples & Best Practices
Large Language Models (LLMs) for Retail
