The article discusses how to optimize Large Language Models (LLMs) for efficiency in terms of cost, latency, and throughput. The author presents a two-pronged approach to optimization: chunking the data and optimizing tokens. By breaking down the data into smaller chunks and minimizing the number of tokens used in the LLM, the author was able to successfully optimize an LLM for a real-world scenario. The article concludes by inviting readers to try these techniques and explore the world of AI further.