Why Your Enterprise RAG Pipeline Is Failing Before the First Query Runs

Most teams building Retrieval-Augmented Generation (RAG) systems invest their engineering effort in two places: the LLM and the retrieval layer. They tune prompts, experiment with embedding models, compare vector databases, and benchmark retrieval precision. When the system underperforms in production, they go back to those same two layers and tune again.