I am always experimenting with how AI/ML can help boost productivity and uncover new insights. A key issue I (and many others) have encountered is “hallucinations.”
AI hallucinations occur when an artificial intelligence system generates information that is false or not grounded in facts. Simply put, AI just makes up random stuff that sounds plausible but isn’t. This can happen when the AI confidently provides answers based on incomplete, misunderstood, or fabricated data.
Imagine how disastrous that can be if used for decision-making!!
Now how do we address that?
This is where Retrieval-Augmented Generation (RAG) comes into the fold. RAG limits hallucinations by grounding responses in reliable, real-world data. RAG combines a large language model (LLM) with a retrieval mechanism that pulls relevant information from trusted sources, such as databases or documents. This ensures that the AI generates answers backed by factual evidence, significantly reducing the likelihood of hallucinations.