Why Does Your RAG Give Wrong Answers When Someone Else's Doesn't? In the first two articles, we built a RAG pipeline that runs. But many people find that while the code works, answer quality is inconsistent — sometimes spot-on, sometimes missing information that's clearly in the document, sometimes drifting off-topic even when the right chunks were retrieved. The problem is usually not the code. It's the parameters . RAG has four core parameters, like four knobs on a radio: Chunk Size : How long is each text chunk? Chunk Overlap : How much do adjacent chunks overlap? Top-K : How many chunks does the retriever return? Embedding Model : How is text converted into vectors? The combination of these four parameters directly determines whether the system can find relevant information and whether that information is enough to answer the question. In this article, we'll use a controlled-variable experiment so you can see the effect of different parameters with your own eyes.…