> For the complete documentation index, see [llms.txt](https://docs.layerlens.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.layerlens.ai/9.-improve-tune-the-system/workflow.md).

# Improve (workflow)

Stage 5. With a baseline score in hand, **improve** by changing what you control: prompts, retrieval, model, judge rubrics. Then re-run.

## The question this stage answers

*"Can I make it better — and prove it?"*

## What to do

1. **Pick one variable.** Don't tune everything at once.
2. **Make the change.** New prompt, swap model, tweak temperature, add a retrieval re-ranker.
3. **Re-run the evaluation.** Same scorers, same judges.
4. **Compare to baseline.** Stratix shows per-dimension deltas.
5. **Iterate.** Keep what wins; revert what doesn't.
6. **Optimize the judges themselves.** GEPA tunes the judge rubric against labels.

## Two kinds of improvement

* **System under test** — the model, prompt, retrieval, etc.
* **Measurement system** — the scorers and judges themselves

Both deserve attention. Improving the measurement system (GEPA) often surfaces issues in the system under test you couldn't see before.

## Common pitfalls

* **Tuning to the eval, not the customer.** If your eval doesn't reflect customer reality, you'll over-fit.
* **Not retiring obsolete examples.** Your eval set should evolve as the product evolves.
* **Treating one improvement as final.** The frontier moves; revisit quarterly.

## Where to next

* [Govern →](/9.-improve-tune-the-system/workflow.md)
* [Tutorial: Optimize a judge with GEPA](/9.-improve-tune-the-system/05-gepa-optimize.md)
* [Stratix Premium — Judge optimization](/9.-improve-tune-the-system/judge-optimization.md)
* [Concept: Judges](/8.-evaluate-score-the-outputs/judges-1.md)


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