A novel architecture for Retrieval-Augmented Generation pipelines, termed loop engineering, is redefining how organizations balance inference costs with output accuracy. Rather than defaulting to expensive flagship models for every generation task, the framework employs an LLM cascade that begins with a lightweight local model, validates the output, and escalates to a premium API only when verification fails. This approach transforms model selection from a static configuration into a dynamic, criteria-driven process. Benchmarks conducted across hundreds of typed fields and twenty local models reveal critical insights that justify the cascade design. Contrary to industry assumptions, parameter count is a poor predictor of performance. A four-billion and a seven-billion parameter model outperformed larger twelve-billion and fourteen-billion variants, while sub-two-billion models frequently failed to generate structured JSON. On raw tasks, the best local models achieved only roughly a third accuracy, making them unviable without safeguards. However, accuracy improved dramatically when business glossaries and field definitions were injected directly into prompts, lifting flagship performance to perfect scores and significantly boosting local model reliability. Notably, hosted flagship APIs demonstrated lower latency than most local deployments, meaning the cascade prioritizes cost reduction and data sovereignty over speed. The cascade architecture operates through a tightly bounded verification loop. A lightweight dispatcher assigns a starting model tier based on three primary criteria: data confidentiality, typed-output reliability, and transformation complexity. Confidential documents are routed exclusively to local models. Every generated output undergoes automated validation checks for structural integrity, source citation accuracy, and semantic alignment. When validation succeeds, the process terminates, allowing cheaper models to handle straightforward lookups at a fraction of flagship costs. When validation fails, the system escalates to a more capable model or decomposes the task. Task decomposition serves as a critical alternative to escalation. Complex numerical conversions, such as scaling abbreviated values, often trigger small model failures. Instead of immediately incurring higher API costs, the system can instruct the local model to locate the source text and delegate the mathematical formatting to deterministic code. This method preserves data privacy by keeping raw documents local, though benchmarks note a slight accuracy trade-off that requires careful measurement. The dispatcher automatically flags fields requiring this split. This generation-side cascade integrates seamlessly with existing enterprise RAG components. It complements question-level dispatch, which pre-assigns model tiers based on query complexity, and mirrors adaptive parsing strategies that also employ a cheap-first, escalate-on-demand methodology. The unified validation layer acts as the arbiter, confirming whether initial tiers suffice or triggering upward mobility. By shifting model selection from a constant configuration to a responsive pipeline stage, loop engineering delivers substantial operational savings without compromising reliability. Organizations can drastically reduce their largest line item, inference costs, while maintaining enterprise-grade accuracy. The framework proves that strategic prompt engineering, rigorous validation, and intelligent task routing outperform raw computational power, establishing a new standard for cost-efficient, secure AI deployment.