The Problem RAG Solves
Examine real failure cases where LLMs hallucinate outdated or wrong information, and understand how grounding answers in retrieved documents fixes these problems.
LLMs Have a Knowledge Cutoff
Every large language model is trained on a snapshot of the internet up to a specific date called the knowledge cutoff. GPT-4o has a cutoff in early 2024. Ask it about events that happened after that date and it will either confess ignorance or, worse, confidently fabricate plausible-sounding but wrong information. For applications that need current or proprietary knowledge, this is a fundamental problem.
The Hallucination Problem
Hallucination occurs when an LLM generates text that sounds authoritative but is factually wrong. Models are trained to produce fluent, coherent text — they are not explicitly trained to refuse when they do not know something. As a result, they fill gaps in knowledge with plausible guesses. Studies show that even the best models hallucinate on knowledge-intensive tasks 10-40% of the time without external grounding.