Large Language Models (LLMs) have revolutionized natural language processing, demonstrating remarkable capabilities in various applications. However, these models face significant challenges, including temporal limitations of their knowledge base, difficulties with complex mathematical computations, and a tendency to produce inaccurate information or “hallucinations.” These limitations have spurred researchers to explore innovative solutions that can enhance LLM performance without the need for extensive retraining. The integration of LLMs with external data sources and applications has emerged as a promising approach to address these challenges, aiming to improve accuracy, relevance, and computational capabilities while maintaining the models’ core strengths in language understanding and generation.