Summary

The absence of long-term memory in generative AI has limited its ability to handle complex, multi-step tasks requiring continuity and adaptability. Retrieval-Augmented Generation (RAG) has emerged as a solution by integrating generative AI with external retrieval systems, enabling these LLM models to dynamically query and incorporate relevant external knowledge. This advancement is a foundational shift that significantly enhances the scalability, accuracy, and application scope of generative AI systems.