> 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/8.-evaluate-score-the-outputs/scorers.md).

# Scorers

The Stratix Premium Scorers surface — manage LLM-backed scorers org-scoped.

{% hint style="info" %}
**Available in Stratix Premium.** Scorers are a logged-in workspace feature at [stratix.layerlens.ai](https://stratix.layerlens.ai). Stratix Public users can browse the catalog but not author or run scorers.
{% endhint %}

## Scorers

Scorers are **LLM-backed graders** — a model plus an evaluation prompt — that you apply across benchmarks. Your org builds a library of them over time.

URL: [`stratix.layerlens.ai/dashboard/scorers`](https://stratix.layerlens.ai/dashboard/scorers)

### Anatomy

A scorer record contains:

* **Name** (3–64 chars) and **description** (10–500 chars)
* **Model** — the LLM that runs the prompt (pick from the catalog or a BYOK model)
* **Prompt** — the evaluation instructions

Scorers are **immutable** once created. To revise a rubric, author a new scorer; this keeps prior evaluation results reproducible against the exact rubric that produced them.

### When to use a scorer vs a judge

* **Scorer** — reusable rubric applied as part of a benchmark or custom-dataset evaluation. Immutable. No versioning, no trace-level surface.
* **Judge** — versioned rubric tuned with labeled examples, applied to traces (and optionally to evaluation runs). Use a [judge](/8.-evaluate-score-the-outputs/judges.md) when you need GEPA tuning or trace-level evaluation.

### Creating a scorer

Click **New scorer**:

1. **Name and description** — the catalog labels.
2. **Pick a model** — frontier for nuanced rubrics; cost-optimized for high-volume, narrow rubrics.
3. **Author the prompt** — include input-variable placeholders (`{{output}}`, `{{expected}}`, `{{context}}`, `{{input}}` as relevant) and tell the model exactly what to return (numeric range, label set, structured JSON).
4. **Test on examples** — paste sample inputs/outputs, verify the scorer returns expected verdicts.
5. **Save** — scorer becomes available in any evaluation in your org.

### Scorers in evaluations

When you create an evaluation, the scoring step lets you stack any number of scorers. Each scorer produces a per-row verdict; the evaluation's overall score is configurable (mean, median, all-pass, etc.).

### Deterministic / code graders

Some checks are inherently deterministic — exact match, regex, JSON-schema validity, Flesch-Kincaid grade, fairness math. These don't fit the LLM-prompt Scorer surface and run as separate **code graders** in the evaluation runtime. See [Custom code grader recipe](https://github.com/LayerLens/gitbook-full/blob/main/08-evaluate/cookbook/custom-code-scorer.md).

### Where to next

* [Concept: Scorers](/8.-evaluate-score-the-outputs/scorers-1.md)
* [Judges](/8.-evaluate-score-the-outputs/judges.md)
* [Evaluations](/8.-evaluate-score-the-outputs/evaluations.md)
* [SDK reference — Scorers](/8.-evaluate-score-the-outputs/scorers.md)