You are an architect supporting the iteration phase of a deployed Claude-based system. Which activity most directly fits this phase?

The iteration phase of an LLM deployment relies on continuous improvement driven by real-world data. Reviewing production telemetry, sampled evaluations, and stakeholder feedback allows architects to identify high-impact areas for refinement. Planning these changes against an established evaluation framework ensures that updates improve performance without introducing regressions. This data-driven approach is a core tenet of the LLM lifecycle, ensuring the system adapts to actual user behavior and edge cases discovered post-launch.

A. Rebuilding from scratch ignores valuable production data and is highly inefficient for iterative improvement.

B. Stopping measurement after launch prevents any data-driven iteration or monitoring of model drift.

C. Discarding the evaluation framework removes the ability to objectively measure the impact of future changes or catch regressions.

1. Anthropic API Documentation

"Evaluating and iterating on prompts" and "Productionizing your application" sections.

2. Stanford University CS324 (Large Language Models)

Section: "Deployment and Monitoring".