Evaluation-Driven Deliberate Practice

Practice only works when it changes future performance. Repetition by itself can reinforce the same mistakes, so I prefer a simple model: practice should be driven by evaluation.

Start with an observable target

“Become better at engineering” is not directly actionable. A useful target describes behavior that can be inspected: diagnose a failure from evidence, produce a design with explicit trade-offs, or complete a change while keeping all quality gates green.

Once the target is observable, the loop becomes clear:

1. Attempt a realistic task.
2. Evaluate the output with a stable rubric or executable check.
3. Identify the smallest recurring weakness.
4. Design the next exercise around that weakness.
5. Repeat until the evidence changes.

Use failures as training data

A failed attempt is valuable only if it affects the next attempt. I keep mistakes close to the workflow: failed assertions become tests, recurring review comments become checklists, and repeated reasoning gaps become examples or skills.

This applies equally to people and agents. Both improve faster when feedback is specific, timely, and connected to the next action.

Measure transfer, not familiarity

Recognition can feel like mastery. The stronger test is whether the skill transfers to a fresh problem without relying on the previous answer. Good evaluation therefore includes unfamiliar cases, delayed recall, and production-like constraints.

The objective is not a higher practice count. It is a shorter distance between intention, evidence, correction, and reliable performance.