The Essential Guide to Effectively Summarizing Massive Documents, Part 2

Part 2 of a series on summarizing large documents using clustering and LLMs. Starting from 1360 chunks of the GitLab Employee Handbook (220,035 tokens), the workflow evaluates K-means cluster quality using silhouette, Calinski-Harabasz, and Davies-Bouldin scores, then visualizes clusters with UMAP dimensionality reduction. Representative chunks are selected per cluster using Euclidean distance to centroids, individually summarized via LangChain map prompts, and then combined into a final summary — reducing context to just 4,219 tokens (98% reduction). The post honestly assesses both strengths (scalable, thematic coverage) and weaknesses (final aggregation narrows topic breadth), and suggests improvements like stronger reduce prompts and multiple representatives per cluster.