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.

Lakehouse Monitoring: A Unified Solution for Quality of Data and AI

Databricks architecture overview

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

Galit Raz on LinkedIn: Real-Time Insights: The Top Three Reasons Why Customers Love Data…

Community How to build RAG Applications that Reduce Hallucinations

Best Practices for LLM Evaluation of RAG Applications A Case Study on the Databricks Documentation Bot : r/llm_updated

Databricks invests in Mistral AI and integrates Mistral AI's models into the Databricks Data Intelligence Platform

Introduction to Databricks Workflows

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

Implementing RAG with Databricks: Efficient AI Enhancement

Emerging Architectures for LLM Applications

✨ Get industry-specific Solution Accelerators for free on Databricks Marketplace! Fast-track your projects with notebooks and sample data., Gabriela (Gabby) Prylinski posted on the topic

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

5 Ways To Style Blue rag & bone Sneakers In Summer + Fall - The Mom Edit

RAG vs Finetuning — Which Is the Best Tool to Boost Your LLM Application?, by Heiko Hotz

Exploring RAG: The Future of AI Interaction.

Advanced RAG for LLMs/SLMs. Retrieval augmented generation (RAG)…, by Bijit Ghosh

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