Developer Builds Minimal RAG Pipeline in 40 Lines to Expose Its Hidden Weaknesses

Software developer Suman has published the first part of a practical series on Retrieval-Augmented Generation (RAG), demonstrating how to build a functional question-answering pipeline in roughly 40 lines of Python. RAG works by retrieving relevant text chunks from a document corpus and feeding them into a large language model as context before generating an answer, allowing models to respond about data they were never trained on. The tutorial uses local sentence-transformer embeddings for free, API-key-free retrieval and OpenRouter for text generation, keeping the implementation transparent without relying on high-level frameworks. A companion runnable notebook is available on Kaggle, making the code accessible to anyone looking to follow along. Suman intentionally highlights where this naive pipeline fails — such as its one-chunk-per-document strategy — framing those weaknesses as the roadmap for improvements in subsequent parts of the series.