Unlocking the Future of AI: Overcoming Key Challenges with Mindful-RAG in Knowledge-Enhanced Generation

Large Language Models (LLMs) have made significant advancements in natural language processing, but they often struggle with knowledge-intensive queries, particularly in domain-specific and factual question-answering tasks.

Retrieval-augmented generation (RAG) systems were developed to address this issue by incorporating external knowledge sources such as structured knowledge graphs (KGs). Despite these advancements, LLMs still face challenges in providing accurate answers even when the necessary information is available.