RAG
Recent items mentioning RAG across the Databricks ecosystem — releases, news, videos, and community Q&A. Updated hourly.
Newer reasoning models from OpenAI and DeepSeek hallucinate more than their predecessors did, pushing RAG back to the front as a core mitigation alongside domain fine-tuning and systematic evaluation 1. Enterprise AI assistants now treat RAG as a standard layer alongside LLMs and agentic frameworks for running data-analysis and pipeline-management workflows 2, while hands-on tutorials are walking practitioners through the full seven-step build — chunking, embedding, similarity search, prompt augmentation — from scratch 3.
Generated daily from the 3 most recent items mentioning RAG. Click any [N] to jump to the source.
What are AI Hallucinations?
What are AI hallucinations, why does it matter, and what can enterprises do about it? Newer reasoning models from OpenAI and DeepSeek are actually hallucinating more than their predecessors, not less, making detection and prevention a must for any production deployment. Enterprises can curb the risk with retrieval-augmented generation, domain-specific fine-tuning, systematic evaluation frameworks, and strong data governance.
What is an AI Assistant?
Enterprise AI assistants use large language models, retrieval-augmented generation, and agentic frameworks to execute workflows ranging from data analysis to autonomous pipeline management. Choosing the right tool requires evaluating data integration depth, governance controls, extensibility, and alignment with your team's existing workflows and skill levels.
NewsRAG Explained + Build a RAG App From Scratch in Python using LLM | Chapter 08
This video teaches the core concepts of retrieval-augmented generation and demonstrates how to build a complete RAG application from scratch in Python using a Groq language model. The tutorial covers a seven-step pipeline including document ingestion, token-based text chunking, vector embedding generation, in-memory storage, similarity search, prompt augmentation, and response generation.
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
