When I first started building enterprise applications with Large Language Models (LLMs), I fell into a trap that almost every developer encounters. I thought that scaling an AI system simply meant refining a single, massive prompt. I wrote complex system instructions, packed the context window with rules, and expected a single stateless API call to act as a researcher, analyst, and copywriter all at once.In production, this monolithic approach failed repeatedly. When processing dynamic data streams, the model flattened nuanced details, skipped critical execution steps, and regularly generated highly confident hallucinations.