Introduction When building Retrieval-Augmented Generation (RAG) systems, it is tempting to focus immediately on embeddings, chunk sizes, vector databases and prompt design. However, segmentation and retrieval behavior are not independent engineering choices. They are constrained by the structure of the data itself. This article explores the structural characteristics of the Wikipedia Movie Plots dataset and…
Introduction Retrieval-Augmented Generation (RAG) systems are often described as linear pipelines: load documents, split text, generate embeddings, store vectors, retrieve context and invoke a language model. This description is conceptually accurate, but architecturally incomplete. When presented as a sequence of steps, the structural decisions that shape system behavior remain implicit. Choices about segmentation, persistence, filtering…
Introduction Chunk overlap is widely treated as a reliable mechanism for preserving semantic continuity between adjacent chunks in Retrieval-Augmented Generation (RAG) pipelines. The intuition is straightforward: reuse part of the end of one chunk at the start of the next so the context isn’t artificially broken. In practice, this mechanism is rarely examined in detail….
