Enterprises Adopt RAG To Reduce Hallucinations

An explainer describes retrieval-augmented generation (RAG) and grounding as cost-effective techniques to reduce LLM hallucinations and keep responses current. It outlines RAG's retrieval, vectorization, similarity scoring, and augmentation steps, contrasts grounding with fine-tuning, and cites OpenAI's acknowledgement of persistent hallucination issues. The piece argues enterprises can use RAG with internal authoritative data to improve answer accuracy without expensive retraining.