Hallucinations in LLMs: Why They Happen and How to Mitigate Them

Large language models can sometimes generate text that is plausible-sounding but factually incorrect or nonsensical—a phenomenon known as "hallucination." This article provides a clear explanation of why LLMs hallucinate, tracing the issue back to their training data and probabilistic nature. We explore the different types of hallucinations and the risks they can pose in real-world applications. More importantly, we discuss a range of strategies for mitigating these issues, including retrieval-augmented generation (RAG), prompt engineering techniques, and fact-checking mechanisms. Understanding and addressing hallucinations is key to building trustworthy AI.