RAG and LLMs: A Knowledge Base Alone Does Not Make Answers Reliable

Many RAG projects appear complete because retrieval, prompting, and generation are wired together. Yet the answers still drift. The weakness is usually not the model. It is the evidence pipeline.

High-quality RAG is not about retrieving more text. It is about retrieving the right text, in the right chunk size, with enough ranking and constraint to keep the model grounded in evidence.

In production, the most dangerous answer is not an empty one. It is a confident one built on the wrong context.