Why LLMs Hallucinate (And How to Actually Reduce It)

An LLM will hand you a completely made-up fact with total confidence, and nothing in the output tells you it's wrong. In a data pipeline, that's dangerous. In this video I break down why LLM hallucination happens, why it's not a bug you can fully patch, and the concrete techniques that actually cut it down in production. 0:00 Confidently wrong, and why it's worse than a normal error 2:30 Why it happens: next-token prediction, not fact retrieval 3:30 Where hallucination bites in data work: fake schema, broken SQL, fabricated citations 5:30 Technique 1: grounding with RAG 9:30 Techniques 2-4: lower temperature, ask for citations or "I don't know," validate programmatically Evaluation and guardrails: catching hallucinations before they ship If this is useful, you'll probably want my earlier video on RAG and chunking first, it's the foundation for the grounding technique covered here. Subscribe for more breakdowns on how AI and data systems actually work under the hood. 🚀 Check Out My Data/AI Courses: https://whop.com/the-data-guy-llc/ Use Code dataguysub for 25% off! 🚀 Get Source Code and Bonus Content: https://patreon.com/TheDataGuy?utm_me... ⚡ Want to work together? Book time on my website: https://thedataguygeorge.com/ 🎬 Watch My Daily Data & AI Shorts:    / @dataandaiguyshortform