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.
Community How to build RAG Applications that Reduce Hallucinations
Michelle (Gress) Rideout on LinkedIn: Creating High Quality RAG Applications with Databricks
Audrey Cain on LinkedIn: Protecting people's health and well-being with AI
Volker Tjaden auf LinkedIn: Nasdaq uses Databricks Lakehouse for BI and ML applications with real-time…
Introducing Databricks Vector Search Public Preview
Improve your RAG application response quality with real-time structured data
Marcelo Sales on LinkedIn: Enhancing your team's performance by building a data culture
Part2: Implementing a RAG chatbot with Vector Search, BGE, langchain and llama2 on Databricks
✨ 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
Kasey Uhlenhuth (@kuhlenhuth) / X
