Retrieval-augmented generation (RAG) is a technique that allows LLMs to incorporate external information from a corpus of documents during the text generation process. RAG mitigates the hallucination problem by grounding LLM responses in verified data sources. The key components of text analytics—text preprocessing, natural language processing, entity extraction, and structuring data—play a critical role in this process. These techniques ensure that data fed into an LLM is clean, relevant, and structured to maximize the accuracy and reliability of the responses. By leveraging organizational knowledge and domain-specific datasets, RAG significantly enhances the performance and trustworthiness of LLM outputs.
DCL's power trio Mark Gross, Tammy Bilitzky, and Rich Dominelli discuss how organizations can mitigate AI hallucinations by employing RAG and structured content. The first two video highlights dive straight into the discussion on RAG and mitigating hallucinations, followed by the full one-hour conversation.