Developers engineering enterprise-grade retrieval-augmented generation systems are abandoning monolithic prompt architectures in favor of modular, dispatcher-driven generation pipelines. This approach replaces unmanageable mega-prompts with a structured composition engine that dynamically assembles generation requests based on parsed question schemas. The framework, positioned as a core component of a four-brick enterprise RAG methodology, ensures reproducible outputs, strict schema enforcement, and comprehensive auditability across complex document workflows. At the foundation of the architecture is a dispatcher that ingests a parsed question and routes it to a preconfigured answer schema. Instead of embedding every possible formatting rule into a single system prompt, the system maintains a fixed, shape-agnostic base covering citation requirements, hallucination prevention, and evidence conflict handling. Domain-specific extraction rules are applied dynamically as modular fragments tailored to the expected answer type, such as monetary amounts, dates, or boolean flags. This additive design prevents prompt bloat, eliminates combinatorial rule conflicts, and ensures that model token consumption remains strictly proportional to the query scope. User prompts are constructed by appending the original question, embedded query keywords, and retrieved passage lines to the composed system instruction. The pipeline supports two execution modes for chunk processing: combined calls for synthesizing information across multiple passages, and sequential calls with early termination for localized fact retrieval. Model temperature is fixed at zero to guarantee deterministic extraction, and all raw API responses are permanently logged with full metadata, enabling precise forensic analysis and compliance auditing without relying on estimated token counts. A significant advancement in this architecture is the implementation of per-field evidence tracking. For multi-field extractions commonly found in compliance triage or financial document processing, the system wraps each extracted value with its own citation span. Downstream validators cross-reference every quoted passage against the source material, automatically flagging hallucinated citations before human review. This granular verification significantly reduces false-positive rates and provides auditors with direct mapping between extracted data and source document lines. To further enhance extraction accuracy, the pipeline incorporates dynamic few-shot retrieval. A curated bank of historically validated question-answer pairs is queried alongside the main document corpus. When a new request closely matches a past example, the system injects the validated response structure directly into the prompt. This technique effectively teaches the model domain-specific formatting rules and normalization standards without requiring continuous prompt engineering or costly model finetuning. The example injection is conditional, skipping insertion when queries fall outside distribution or when data sensitivity restricts prompt exposure. By decoupling prompt assembly from model inference and enforcing strict structural validation at every stage, this generation framework delivers a reliable, auditable, and cost-efficient solution for enterprise RAG deployment. The architecture shifts focus from open-ended conversational flexibility to deterministic, traceable information extraction, aligning generative AI outputs with the stringent reliability, security, and compliance requirements of modern corporate data pipelines.