Reducing LLM Hallucinations Using Retrieval-Augmented Generation (RAG)
Large language models (LLMs) have revolutionized the field of natural language processing (NLP), providing impressive capabilities in tasks such as translation, summarization, and conversational AI. However, one persistent issue with LLMs is their tendency to generate “hallucinations”—outputs that are factually incorrect or nonsensical. Addressing these hallucinations is crucial for ensuring the reliability and accuracy of […]