RAG
Recent items mentioning RAG across the Databricks ecosystem — releases, news, videos, and community Q&A. Updated hourly.
End-to-End RAG Workflow: How Retrieval Augmented Generation Works
Databricks now offers a five-stage RAG workflow for connecting LLMs to external knowledge bases, enabling accurate, domain-specific answers without model retraining. Production RAG requires careful selection of embedding models, vector database indexing, chunking strategies, and hybrid search, with independent evaluation of retrieval precision and generation faithfulness.
Data Engineering for AI: A Practical Guide for Data Professionals
Data engineering for AI demands new skills and a shift from traditional BI to managing large-scale, unstructured, and real-time data pipelines for ML and generative AI. Master feature engineering, vector databases, RAG, and ethical data practices alongside automation, observability, and unified data architecture to build production-grade AI solutions.
Tutorials
