Large language models (LLMs) have a hunger for data. The more information they can access, the better their responses. But feeding these models massive amounts of text, like thousands of customer reviews, can be expensive and slow. Traditional methods like Retrieval Augmented Generation (RAG) simply pull all relevant information, which quickly leads to a "token deluge" – too many words for the LLM to handle efficiently. Enter CRAG, or Clustered Retrieved Augmented Generation, a clever new technique to streamline this process. Imagine trying to understand what thousands of people think about a product. Reading every single review would take forever. CRAG works by first grouping similar reviews together, like creating summaries of common opinions. Then, it summarizes each group, distilling the key themes. Finally, it combines these summaries into a concise overview. This drastically reduces the number of tokens, or words, the LLM needs to process. In tests, CRAG reduced the number of tokens needed by a whopping 46% to over 90% compared to RAG. This translates to significant cost savings, especially for large-scale applications. For example, querying GPT-4 with CRAG cost more than ten times less than using RAG. What's more, the quality of the LLM's responses remained comparable, even with the reduced input. CRAG isn't just about saving money; it also makes LLMs faster. By reducing the input size, the model can generate responses more quickly, improving the user experience. While CRAG shows great promise, there's still room for improvement. Future research could explore different clustering methods and more powerful summarization models. Fine-tuning these models on specific tasks could further enhance their performance. CRAG represents a significant step towards making LLMs more practical and cost-effective for real-world applications. By taming the token deluge, it unlocks the potential of LLMs to analyze vast amounts of text efficiently, opening doors to new possibilities in fields like market research, customer service, and content creation.