Enterprise developers are increasingly recognizing that the structural integrity of Retrieval-Augmented Generation pipelines hinges on how user queries are initially processed. A new technical framework highlights a critical flaw in mainstream RAG implementations: the tendency to treat natural language questions as flat, unstructured strings sent directly to vector databases. This naive approach frequently results in silent partial answers, excessive context bloat, and untraceable prompt degradation in production environments. To resolve these issues, developers are shifting toward a relational question-parsing architecture that structures queries before retrieval begins. The proposed system replaces string-based routing with a typed relational schema, typically represented as a structured dataframe. Each query is parsed into a single row containing five core columns: keywords, scope, shape, decomposition pattern, and clarification requirements. This structured representation allows the pipeline to enforce context discipline by calculating precise context windows measured in document lines rather than characters or pages. Factual lookups receive minimal surrounding context, while listing or sequential queries are allocated broader forward windows, ensuring the model receives only what it needs to generate accurate, verifiable outputs. The architecture derives two specialized briefs from this parsed data, separating responsibilities for downstream components. The retrieval brief contains only operational filters, such as keywords and structural hints, while the generation brief carries intent and output formatting instructions. This division prevents retrieval systems from being cluttered with generation-specific constraints and eliminates the need for the language model to re-parse raw queries. Framework architects emphasize six foundational practices to stabilize enterprise RAG systems. First, treating queries as relational entities mirrors the structure of the underlying documents, enabling precise filtering and joins across both data sides. Second, system capabilities should expand through schema additions rather than branching conditional code, keeping feature integration linear and maintainable. Third, replacing fuzzy embeddings with curated expert dictionaries for synonym mapping drastically reduces retrieval drift and accelerates matches against domain-specific corpora. Fourth, explicitly categorizing compound questions into independent, sequential, unified, or conditional patterns prevents partial answers by forcing the pipeline to decompose or chain requests appropriately. Fifth, routing decisions must rely on deterministic rule-based dispatchers rather than self-planning language models, ensuring consistent, auditable execution paths across repeated compliance checks. Finally, intent detection remains deliberately minimal in initial deployments, focusing on core dispatch triggers like factual lookups and cross-references, with advanced taxonomies reserved for later phases. The parsing brick operates independently of industry verticals, relying on interchangeable expert dictionaries to adapt to sector-specific terminology while preserving a universal dispatch and audit trail. Implementation resources, including runnable notebooks and reference code, are publicly available for enterprise integration. By elevating question parsing from an afterthought to a disciplined structural layer, organizations can build RAG systems that deliver auditable, predictable, and context-aware responses at scale.