Image generation works beautifully in demos and fails the moment real users hit it. Latency spikes, models collapse under specific prompts, and low quality images slip into production at exactly the wrong time. Before applying the right patterns, the pipeline was slow, inconsistent, and had many unpredictable failures. After restructuring it, latency improved, cache hits reached 50 percent, and failure rates dropped to near zero, even under load. The shift came from three techniques: a strategy-based router that selects the right model per use case, LLM-generated cache keys that massively boost performance, and evaluation filters that prevent weird or unusable images from ever surfacing. These solutions come directly from handling failures at scale. You will leave with a production blueprint for image generation that stays fast, stable, and predictable at scale.
Data Engineer at mavens by Zynga