RAG is a methodology that enhances the capabilities of large language models (LLMs) by integrating external knowledge sources into the generation process. By retrieving relevant information from knowledge bases or document collections, RAG enables LLMs to produce more accurate and contextually aware responses, mitigating the issue of hallucinations. The process involves a two-stage approach: first, … Continue reading RAG-Retrieval Agumented Generation