Introduction
Every AI engineer knows the loop — prompt tweaks, evals, regressions, repeat. It feels like going in circles. The problem isn't the loop; it's not knowing whether each circle is tighter than the last. Tracing, scoring, and prompt experiments turn iteration from an act of faith into something measurable: traces show what happened, scores show whether it was better, prompt experiments show why.
Why this matters
- Without measurement, a stalled loop and a converging loop look identical.
- Eval scores aren't enough; you need score deltas across changes.
- Prompt experiments without traces are just vibes.
- Convergence is a property of your tooling, not your willpower.
Core concepts
Trace, score, experiment
Trace each run, score against an eval, treat each prompt change as an experiment with a hypothesis.
Score deltas, not absolutes
A 0.62 absolute score is uninterpretable; +0.04 over yesterday is informative.
Cohort comparisons
Slice scores by user, prompt template, model. The aggregate hides the regressions.
Practical patterns
Versioned prompts
Every prompt change is an artefact with a version, hypothesis, and result.
Auto-eval on every change
CI runs the eval suite on every prompt or model change.
Drift alerts
Alert when production scores diverge from CI scores.
Pitfalls to avoid
- Iterating without writing down what you changed.
- Comparing this week's eval to last week's with a different eval set.
- Eyeballing outputs in lieu of scoring.
Key takeaways
- 1Make iteration measurable; that's the whole game.
- 2Score deltas, slice by cohort, version everything.
- 3A loop you can't measure isn't a loop, it's a habit.
Go deeper · external resources
Curated reading list to take you from primer to practitioner. All links are external and free to read.