Large Language Models (LLMs) are revolutionizing how we access information, but they sometimes stumble when faced with complex or niche queries. This is because they can hallucinate or present outdated information from their internal knowledge stores. Retrieval Augmented Generation (RAG) helps by pulling in external knowledge to supplement the LLM's internal database. However, these external documents can be lengthy and full of irrelevant information, creating a new challenge: how can an LLM efficiently sift through all this extra noise? Researchers are exploring a fascinating new approach called concept-based RAG, which uses a technique called AMR-based concept distillation. Imagine reading a long document and highlighting only the key concepts. That's essentially what this technique does. It uses Abstract Meaning Representation (AMR) to break down retrieved documents into a concise set of core concepts, filtering out the noise and allowing the LLM to focus on the most relevant information. This approach has shown promising results, especially when dealing with multiple supporting documents. By distilling the essence of the information, LLMs can provide more accurate and efficient answers, even when faced with a mountain of text. This research opens exciting new possibilities for improving the reliability and efficiency of LLMs, paving the way for more sophisticated and trustworthy AI-powered information retrieval systems. Future research will likely explore how to further refine concept distillation and how to best integrate it with different types of LLMs, potentially leading to more powerful and versatile AI assistants.