1 - 02 How Retrieval Augmented Generation Fixed LLM Hallucinations

The source material, an excerpt from a transcript of the IBM Technology video titled "What is Retrieval-Augmented Generation (RAG)?," explains a framework designed to enhance the accuracy and timeliness of large language models (LLMs). Marina Danilevsky, a research scientist at IBM Research, describes how LLMs often face challenges such as providing outdated information or lacking sources for their responses, which can lead to incorrect answers or hallucinations. The RAG framework addresses these issues by integrating a content repository that the LLM accesses first to retrieve relevant information in response to a user query. This retrieval-augmented process ensures that the model generates responses based on up-to-date data and can provide evidence to support its claims.