This availability of fake news in social media and other networks requires enhanced methods for detecting and handling them. This research proposes a scheme that utilizes a retrieval-augmented generation (RAG) framework to provide improved fake news classification through a retriever-generator system. By integrating the external information, FAISS identifies relevant information from the Hugging Face Transformers to enrich the classification problem sometimes not fully addressable by unsupervised ML systems. It includes feature extraction, retriever initialization, and generators modified from original systems to provide correct results. This approach works better in differentiating genuine news content from fake news content, and generally contributes toward an ongoing fight of fake news in the real world.