> 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/02-first-judge.md).

# Tutorial 2: Build your first judge

Tutorial 2 — Build, test, and apply your first LLM judge.

**Time:** \~20 minutes **Level:** Beginner-intermediate **You'll build:** A custom helpfulness judge, tested against examples, applied to an evaluation.

## What you'll learn

* How to author a judge rubric
* How to test a judge against examples
* How to apply a judge to an evaluation
* When to GEPA-optimize (next tutorial)

## Prerequisites

* [ ] Completed [Tutorial 1](/8.-evaluate-score-the-outputs/01-first-evaluation.md)
* [ ] At least 10 example outputs you can label "good" or "bad"

## Step 1: Define the dimension

Write down — in plain English — what you're trying to grade. Keep it specific. "Helpfulness for customer-support questions about billing." Not just "helpful."

## Step 2: Create the judge via SDK

```python
from layerlens import Stratix

client = Stratix()

judge = client.judges.create(
 name="billing-support-helpfulness",
 evaluation_goal="Rate whether the response helps the customer resolve a billing question. The response should: (1) directly address the question, (2) provide actionable next steps, (3) be polite and professional.",
)
print("Judge:", judge.id)
```

## Step 3: Test on examples

```python
sample_input = "I was charged twice this month. What do I do?"
sample_output = "Sorry to hear that. Please email billing@company.com and include the dates of both charges. We'll refund the duplicate within 3-5 business days."

verdict = client.judges.run(
 judge_id=judge.id,
 input=sample_input,
 output=sample_output,
)
print("Verdict:", verdict.score, verdict.reasoning)
```

## Step 4: Iterate the rubric

If the verdict doesn't match your intuition, edit the rubric and re-test.

```python
client.judges.update(
 judge_id=judge.id,
 evaluation_goal="(updated rubric...)",
)
```

## Step 5: Apply to an evaluation

```python
evaluation = client.evaluations.create(
 model=model,
 benchmark=benchmark,
 judges=[judge.id],
)
result = client.evaluations.wait_for_completion(evaluation)
print(f"Helpfulness pass rate: {result.judge_results[judge.id].pass_rate}")
```

## Step 6 (optional): GEPA-optimize

If you have ≥30 labeled examples, jump to [Tutorial 5](/9.-improve-tune-the-system/05-gepa-optimize.md) to lift judge agreement.

## What's next

* [Tutorial 3: CI/CD quality gates](/6.-build-wire-your-code/03-cicd-gates.md)
* [Tutorial 5: GEPA-optimize a judge](/9.-improve-tune-the-system/05-gepa-optimize.md)
* [Concept: Judges](/8.-evaluate-score-the-outputs/judges-1.md)