What Is Data Grounding in AI? A Complete Guide

What is data grounding, and how does it stop LLMs from making things up? If you build with LLMs, you already know the failure mode. The model answers with total confidence, and half of it is made up. Outdated facts, invented sources, details that were never true. Scaling to a bigger model won't fix that, because the problem isn't intelligence. The model just has no way to check what's true right now. That is what data grounding does. In this video, Andrius from Oxylabs explains how grounding works: it connects an LLM's answers to trusted, current external data at query time, so the model reasons over real facts instead of stale training memory. He walks through the grounding workflow, then tackles the two questions people always ask, grounding vs RAG and grounding vs fine-tuning, and shows exactly where each one fits. You will also see where grounding data comes from. Fresh, large-scale web data is what makes AI grounding work, and web scraping is the layer that collects it. Whether you call it data grounding or grounding in AI, the idea holds: give the model real facts, and the LLM hallucinations start to disappear. 🔧 TRY OXYLABS WEB SCRAPER API Web Scraper API: https://oxy.yt/dQO3 📚 RESOURCES MENTIONED Oxylabs blog about Data Grounding in AI: https://oxy.yt/iQPs Oxylabs documentation: https://developers.oxylabs.io 🎥 RELATED VIDEOS What Is RAG? Retrieval-Augmented Generation Explained: https://oxy.yt/eQAa AI Agents Explained: A Complete Beginner's Guide: https://oxy.yt/4QS9 💬 CONNECT WITH OXYLABS Website: https://oxylabs.io LinkedIn: https://www.linkedin.com/company/oxylabs-io Support: [email protected] ⏳ TIMESTAMPS 0:00 Intro 0:47 Why LLMs hallucinate 1:38 What is data grounding? 2:15 How the grounding workflow works 3:09 Data grounding vs RAG 4:09 Data grounding vs fine-tuning 5:09 Where grounding data comes from 5:59 Benefits recap 6:46 Outro #DataGrounding #AIGrounding #LLM #LLMHallucinations #GroundingVsFineTuning #RAG #LargeLanguageModels #Oxylabs #2026 © 2026 Oxylabs. All rights reserved.