RAG Isn’t Enough — I Built the Missing Context Layer That Makes LLM Systems Work

Standard Retrieval-Augmented Generation (RAG) systems often fail in multi-turn conversations because they lack control over what enters the LLM's context window. A proposed solution is a "context engineering" layer that sits between retrieval and prompt construction to manage what the model sees. This layer's architecture includes a hybrid retriever, a re-ranker for prioritizing documents, and a memory system with exponential decay to manage conversational history. By explicitly controlling memory, compression, and token budgets, this system makes LLM applications more robust and coherent.