A recent engineering development by Piero Paialunga, Data Scientist at The Trade Desk, introduces a scalable two-stage architecture for LLM-powered recommendation systems. Published as a Python-based implementation, the framework resolves a longstanding industry trade-off between computational cost, retrieval speed, and semantic precision. Generative AI models excel at contextual understanding but remain prohibitively expensive when applied to large-scale datasets. Paialunga’s solution addresses this limitation through a funnel design that isolates heavy computation from initial data retrieval. The system operates across eight U.S. metropolitan areas, utilizing a synthetic dataset of ten thousand records to demonstrate reproducibility. The architecture separates processing into two distinct phases. The first stage employs a lightweight, rule-based geographic filter to rapidly identify nearby options. This high-recall step processes the entire dataset locally, narrowing the selection to fifty candidates without generating API calls or token expenses. The filtered subset proceeds to the second stage, where a large language model performs high-precision semantic reranking. By restricting the LLM input to a highly relevant shortlist, the system dramatically reduces computational overhead. The model evaluates each candidate against natural language queries, assigning a standardized fit score and providing transparent reasoning for its rankings. Object-oriented Python scripts handle data generation, geographic calculations, and OpenAI API integration, ensuring the pipeline remains maintainable and reproducible. Testing across multiple urban markets confirms the architecture efficacy. When processing complex requests involving dietary restrictions, budget parameters, and ambiance preferences, the initial geographic filter successfully eliminates irrelevant inventory. The subsequent LLM reranking stage dynamically promotes culturally and contextually appropriate venues, even when exact matches are unavailable. Partial matches are explicitly scored and ranked lower, delivering actionable alternatives to end users. The resulting system establishes a practical blueprint for production-grade AI recommendations. By decoupling scale from intelligence, organizations can maintain high-throughput retrieval systems while reserving advanced generative reasoning for targeted evaluations. This approach directly mitigates the cost and latency barriers typically associated with enterprise LLM deployment, offering engineering teams a verified method to integrate artificial intelligence into high-volume data pipelines without compromising performance or budget constraints.