20-minute course on why LLMs hallucinate and how to prevent it. Covers RAG architecture, citation systems, knowledge base hygiene, and evaluation rubrics. Free certificate of completion.
Welcome to Understanding & Preventing AI Hallucinations.
AI hallucinations aren't edge cases—they're a fundamental characteristic of how large language models work. When your chatbot invents a refund policy or your research tool fabricates citations, you're facing real liability, not just embarrassment.
Hallucination rates have improved dramatically. Industry benchmarks show a decline from approximately 38% in 2021 to around 8% today. But 8% is still unacceptable for most business applications—and you can push that number much lower with the right approach.
We'll start by understanding why LLMs hallucinate—it's not a bug, it's how they work. Then we'll look at real-world costs when organisations got this wrong. The bulk of the training covers Sasha's approach: architectural solutions, not just better prompts.
Anyone using Sasha, considering AI tools, or responsible for AI governance in their organisation. No technical background required.
Every large language model hallucinates. ChatGPT, Claude, Gemini, they all generate confident, fluent text that can be entirely fabricated. For enterprise use, this isn't a minor inconvenience. It's a compliance risk, a reputational risk, and a liability risk that most organisations haven't adequately addressed.
This course explains why AI hallucinations happen at an architectural level, not just at the surface. You'll learn why better prompting alone doesn't solve the problem, and what systemic approaches, retrieval-augmented generation, citation systems, evaluation rubrics, actually reduce hallucination rates in production.
The course takes approximately 20 minutes and includes practical techniques you can apply immediately to any AI deployment.
Practical hallucination prevention techniques
Accountability when AI gets it wrong
18 checks before you trust AI output
Start here if you're new to AI
Large language models generate text by predicting the next most likely token based on patterns in training data. They have no understanding of truth, only statistical probability, which means they can produce confident text that is entirely fabricated.
The most effective approach is architectural: grounding AI responses in verified data using RAG, implementing citation systems, and building evaluation rubrics that check for accuracy, correctness, and completeness.
An AI error is a factual mistake. A hallucination is when the model fabricates plausible-sounding information with no basis in reality. Hallucinations are more dangerous because the AI presents them with the same confidence as accurate information.