Six Architecture Fixes That Cut LLM Token Costs and Latency in Production

As large language models move from experiments to core backend infrastructure, a common anti-pattern has emerged: developers stuffing excessive raw context into prompts to meet product requirements. Engineers at Fanziz encountered this firsthand while building real-time features like personalized news feeds and live commentary, facing spiking inference costs and latency bottlenecks. To address this, they implemented six architectural shifts including retrieval-augmented generation (RAG), context caching, and tiered prompt engineering based on task complexity. They also adopted modular prompt design, treating prompt components as reusable micro-modules assembled dynamically per request rather than relying on a single bloated system prompt. The core principle across all fixes is maximizing output quality while minimizing the data payload sent to the model, rather than defaulting to larger or more expensive models.