Cost-Aware Structured Generation: High-Fidelity Synthesis via Hybrid RAG and Adaptive Conditional Compute

Deploying large language models (LLMs) for structured content generation presents a fundamental tension between quality requirements and computational costs. Standard retrieval-augmented generation (RAG) approaches struggle with complex multi-hop domain questions due to context loss and retrieval noise, while state-of-the-art models impose prohibitive per-query costs at scale. We introduce Cost-Aware Structured Generation, a domain-agnostic 14-stage pipeline that integrates Hybrid RAG with Reciprocal Rank Fusion, conditional compute routing based on query difficulty, and adaptive voting strategies with domain-specific weighting. Our architecture achieves 93.5% overall precision with a hallucination rate of only 0.31% (1 error in 325 human-reviewed samples from 2021-2025) on a validation dataset of 1,200+ complex domain questions spanning 2007-2025, requiring 200+ field structured outputs. Through intelligent routing that selectively invokes expensive models only for high-complexity queries, we demonstrate 84% improvement in content richness while reducing costs by 50.4% compared to baseline Self-Consistency ensembles. Validation on aerospace engineering certification exams demonstrates production viability; the architecture generalizes to any domain requiring high-fidelity structured generation (legal, medical, financial, educational). This work establishes that architectural engineering—not merely model scaling—provides a viable pathway to production-grade structured generation with strict precision requirements.