AI Workflows That Actually Ship in Production

Many AI features fail because they skip operational design. The first step is defining one concrete workflow and one measurable success metric.

Prompting is only a small part of the system. Robust AI products need retrieval quality checks, fallback behavior, and confidence-aware routing.

Evaluation should run continuously, not as a one-time task. Build small benchmark sets from real production cases and monitor drift over time.

The result is predictable AI delivery: fewer surprises, better user trust, and clearer ROI.