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

RAG systems fail when conversation history grows because they lack control over what enters the context window, leading to dropped documents and memory overflow within just a few turns. A developer created a "context engine" that sits between retrieval and prompt construction, using hybrid retrieval, re-ranking, exponential memory decay, and intelligent compression to manage token budgets and maintain coherent multi-turn conversations.