Recommender Systems (RSs) play a pivotal role in delivering personalized recommendations across various domains, from e-commerce to content streaming platforms. Recent advancements in natural language processing have introduced Large Language Models (LLMs) that exhibit remarkable capabilities in understanding and generating human-like text. RS are renowned for their effectiveness and proficiency within clearly defined domains; nevertheless, they are limited in adaptability and incapable of providing recommendations for unexplored data. Conversely, LLMs exhibit contextual awareness and strong adaptability to unseen data. Combining these technologies creates a potent tool for delivering contextual and relevant recommendations, even in cold scenarios characterized by high data sparsity. The proposal aims to explore the possibilities of integrating LLMs into RS, introducing a novel approach called Retrieval-augmented Recommender Systems, which combines the strengths of retrieval-based and generation-based models to enhance the ability of RSs to provide relevant suggestions.