> 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.md).

# 8. Evaluate — score the outputs

- [Evaluate (workflow)](https://docs.layerlens.ai/8.-evaluate-score-the-outputs/workflow.md): Stage 4 — Evaluate. Score outputs and traces with scorers and judges.
- [Premium surfaces](https://docs.layerlens.ai/8.-evaluate-score-the-outputs/premium-surfaces.md)
- [Evaluations](https://docs.layerlens.ai/8.-evaluate-score-the-outputs/evaluations.md): The Stratix Premium Evaluations surface — create, run, browse, and compare private evaluations.
- [Judges](https://docs.layerlens.ai/8.-evaluate-score-the-outputs/judges.md): Build, test, and manage LLM-as-a-judge graders in Stratix Premium.
- [System judges](https://docs.layerlens.ai/8.-evaluate-score-the-outputs/system-judges.md): System judges shipped with Stratix Premium — names, evaluation goals, default models, and how to use them.
- [Scorers](https://docs.layerlens.ai/8.-evaluate-score-the-outputs/scorers.md): The Stratix Premium Scorers surface — manage LLM-backed scorers org-scoped.
- [Trace evaluations](https://docs.layerlens.ai/8.-evaluate-score-the-outputs/trace-evaluations.md): Score uploaded traces with scorers and judges — recurring, on-demand, agentic.
- [Spaces](https://docs.layerlens.ai/8.-evaluate-score-the-outputs/spaces.md): The Stratix Premium Spaces surface — save and re-run evaluation configurations as conditions change.
- [Concepts](https://docs.layerlens.ai/8.-evaluate-score-the-outputs/concepts.md)
- [Mental model](https://docs.layerlens.ai/8.-evaluate-score-the-outputs/mental-model.md): The Stratix mental model — catalog, engine, judges, traces, and how data moves through the platform.
- [Evaluations](https://docs.layerlens.ai/8.-evaluate-score-the-outputs/evaluations-1.md): An evaluation in Stratix — what it actually is, what it produces, how it's stored.
- [Evaluation anatomy](https://docs.layerlens.ai/8.-evaluate-score-the-outputs/evaluation-anatomy.md): The anatomy of a Stratix evaluation — every field, every state, every artifact.
- [Judges](https://docs.layerlens.ai/8.-evaluate-score-the-outputs/judges-1.md): LLM-as-a-judge in Stratix — building, running, and optimizing judges with GEPA.
- [Scorers](https://docs.layerlens.ai/8.-evaluate-score-the-outputs/scorers-1.md): LLM-backed scorers in Stratix — model + prompt, when to use them, when to use a judge instead.
- [Evaluation spaces](https://docs.layerlens.ai/8.-evaluate-score-the-outputs/evaluation-spaces.md): Evaluation spaces — saved configurations you re-run as conditions change.
- [Agentic evaluation](https://docs.layerlens.ai/8.-evaluate-score-the-outputs/agentic-evaluation.md): Agentic evaluation — pre- and post-deployment quality gates for multi-step AI agents.
- [Tutorials](https://docs.layerlens.ai/8.-evaluate-score-the-outputs/tutorials.md)
- [Tutorial 1: First evaluation in 10 minutes](https://docs.layerlens.ai/8.-evaluate-score-the-outputs/01-first-evaluation.md): Tutorial 1 — Run your first end-to-end Stratix evaluation in 10 minutes.
- [Tutorial 2: Build your first judge](https://docs.layerlens.ai/8.-evaluate-score-the-outputs/02-first-judge.md): Tutorial 2 — Build, test, and apply your first LLM judge.
- [Tutorial 4: Score live traces](https://docs.layerlens.ai/8.-evaluate-score-the-outputs/04-score-traces.md): Tutorial 4 — Score production traces continuously with Stratix trace evaluations.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.layerlens.ai/8.-evaluate-score-the-outputs.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.