RAG, MCP, and Fine-Tuning Are Not Rivals — Each Serves a Distinct Layer

A technical discussion from DEV Community clarifies that RAG, MCP, and fine-tuning are commonly misunderstood as competing approaches, when in fact each operates at a different layer of an AI system. RAG functions as the knowledge base layer, retrieving relevant document excerpts from tools like Notion or Confluence at query time without retraining the model. MCP acts as the connector that allows an IDE or agent harness to request those retrieved excerpts from the knowledge base. Fine-tuning, by contrast, shapes the style and format of model responses and is not a substitute for maintaining an accurate, up-to-date knowledge base. The recommended order of implementation is: build the knowledge base first, add retrieval via RAG, connect it through MCP, and only consider fine-tuning if output formatting remains problematic after prompt and schema adjustments.