Over the last year, large language models shifted from experimental prototypes to core backend infrastructure. As feature sets expand, an anti-pattern emerges across engineering teams: solving every product requirement by shoving more raw context into the prompt.
While building real-time, data-intensive features for Fanziz spanning personalized news feeds, semantic search, and dynamic live commentary. we ran directly into the real-world constraints of this approach: spiking inference costs, degraded throughput, and severe latency bottlenecks.
Throwing a larger context window or a more expensive model at the problem is rarely the right engineering fix. Instead, the real architectural challenge is: How do we maximize output quality while minimizing the payload sent over the wire?
Here are the six production shifts we implemented to streamline our LLM pipeline.
1. Retrieve Precision Context, Don't Dump Raw Data
