Larger Context Windows Don’t Fix RAG — So I Built a System That Does

Retrieval-Augmented Generation (RAG) systems are ineffective for performing accurate computational tasks like aggregations on structured data. Increasing the LLM's context window does not fix this problem and instead makes incorrect answers appear more detailed and authoritative, a phenomenon the author calls "Error Observability Collapse." The core issue is that RAG provides a partial, text-based view of data to a model that is not a true calculation engine. The proposed solution is a semantic engine that identifies computational queries and routes them to a deterministic, full-scan process to ensure accuracy by operating on the entire dataset.