Enterprise AI development is shifting from experimental prompt engineering to deterministic data pipelines, with the latest architectural advance focusing on eliminating large language model hallucination at the generation stage of Retrieval Augmented Generation. Rather than relying on fragile natural language instructions, production systems are now implementing strict typed answer contracts that force generative models to output structured, auditable data. The approach treats the generation component of an enterprise RAG pipeline as a controlled execution environment. By replacing open-ended text prompts with programmatic schemas, engineers define exactly how information must be structured before the model processes a query. Instead of returning free-form prose, the model populates predefined types such as Amount, DateValue, TableValue, and Boolean primitives. This eliminates downstream parsing overhead and ensures that financial figures, timestamps, and tabular data arrive in machine-readable formats ready for direct database insertion. Each extracted value is bound to specific line-number citations, allowing retrieval systems to verify claims against source documents without relying on unverified text summaries. To further constrain fabrication, the schema integrates self-assessment and pipeline-feedback fields directly into the output structure. The model evaluates its own extraction, reporting confidence scores, extraction methods, and evidence conflicts. Crucially, it signals whether the retrieved context was complete or if ambiguities require clarification. This turns the LLM into an active diagnostic tool rather than a passive generator, enabling the pipeline to automatically trigger broader retrieval searches or flag contradictory amendments before presenting results to users. Recognizing that self-evaluation alone cannot guarantee accuracy, the architecture introduces programmatic completeness checks. Because language models can only judge what they see, a truncated document context may falsely appear complete. To solve this, retrieval systems now pull an additional overlap page alongside the primary query results. While the model generates its response from the initial context, the pipeline examines the trailing page to verify whether lists or clauses were artificially cut off. If the overlap reveals continuation content rather than a new section, the system automatically rejects the partial result and initiates a refined search. Enforcement of this contract relies on constrained decoding at the API level. Using structured output modes, major platforms validate the model response against the schema in real time, rejecting malformed requests before they reach downstream services. This approach significantly outperforms traditional JSON prompts, where formatting errors routinely break integration. The methodology is accessible across proprietary and open-source models, provided grammar-constrained decoding is enabled. The transition toward typed generation marks a pivotal step in enterprise RAG maturity. By treating model output as a strict data contract rather than conversational text, organizations gain verifiable audit trails, deterministic downstream workflows, and a measurable reduction in fabrication. As document intelligence systems scale, this shift from probabilistic text generation to structured data execution will define the reliability threshold for production AI deployments.