Prompt Engineering Iteration overview

Focused iteration loop for systematically improving the quality and reliability of prompts powering AI-driven product features. The ML engineer hypothesises changes to the system prompt, few-shot examples, or instruction structure, then runs an automated evaluation suite to measure impact against quality metrics. A version- controlled prompt registry tracks all variants and their eval scores. Winning candidates graduate to a shadow-traffic A/B test in production, and only prompts that improve user-facing metrics are promoted to the live configuration.