Retrieval-Augmented Generation (RAG) techniques enhance LLMs by integrating external knowledge sources, which improves their performance in tasks requiring up-to-date or specialized information.
RAG transforms how we interact with large language models by enabling focused, relevant retrieval rather than feeding them entire documents, leading to more accurate responses.
The advancements in deploying smaller-scale Large Language Models (LLMs) on edge devices face challenges like memory limitations but initiatives like MLC LLM allow compatibility across various hardware.
