article , we planned to tackle one of the main challenges in document summarization, i.e., handling documents that are too large for a single API request. We also explored the pitfalls of the infamous ‘Lost in the Middle’ problem and demonstrated how clustering techniques like K-means can help structure and manage the information chunks effectively. We divided the GitLab Employee Handbook into chunks, used an embedding model to convert those chunks of text into numerical representations called vectors. Now, in the long overdue (sorry!) Part 2, we will get to the meaty (no offense, vegetarians) stuff, playing with the new clusters we created. With our clusters in place, we will focus on refining summaries so that no critical context is lost. This article will guide you through the next steps to transform raw clusters into actionable and coherent summaries. Hence, improving current Generative AI (GenAI) workflows to handle even the most demanding document summarization tasks!…