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Judge Alignment Quickstart

The judge_alignment template measures how well an LLM-as-judge aligns with human evaluation standards.

Create the Project

ragas quickstart judge_alignment
cd judge_alignment

Install Dependencies

uv sync

Set Your API Key

export OPENAI_API_KEY="your-openai-key"

Run the Evaluation

uv run python evals.py

Project Structure

judge_alignment/
├── README.md              # Project documentation
├── pyproject.toml         # Project configuration
├── evals.py               # Evaluation workflow
├── __init__.py            # Python package marker
└── evals/
    ├── datasets/          # Test datasets
    ├── experiments/       # Evaluation results
    └── logs/              # Execution logs

What It Evaluates

The template evaluates LLM judge alignment:

  • Scenario: Pre-existing responses are evaluated by an LLM judge
  • Human Labels: Ground truth pass/fail labels
  • LLM Judge: Evaluates same responses with grading criteria
  • Alignment Metric: Agreement between human and LLM judgments

Understanding the Code

Judge Metrics (evals.py)

Two judge implementations to compare:

# Baseline judge (simple prompt)
accuracy_metric = DiscreteMetric(
    name="accuracy",
    prompt="Check if response contains points from grading notes...",
    allowed_values=["pass", "fail"],
)

# Improved judge (enhanced with abbreviation guide)
accuracy_metric_v2 = DiscreteMetric(
    name="accuracy",
    prompt="""Evaluate if response covers ALL key concepts...

    ABBREVIATION GUIDE:
    • Financial: val=valuation, post-$=post-money, rev=revenue...
    • Business: mkt=market, reg=regulation...
    """,
    allowed_values=["pass", "fail"],
)

The Evaluation

Tests alignment with human judgment:

@discrete_metric(name="alignment", allowed_values=["aligned", "misaligned"])
def alignment_metric(llm_judgment: str, human_judgment: str):
    # Compares LLM judge output with human label
    return "aligned" if llm_judgment == human_judgment else "misaligned"

Test Data

The dataset includes: - Pre-evaluated responses - Human pass/fail labels - Grading notes with expected points - Various abbreviations and business terminology

Use Cases

Compare Judge Versions

Run experiments with both judges:

# Test baseline judge
results_v1 = await run_with_judge(accuracy_metric)

# Test improved judge
results_v2 = await run_with_judge(accuracy_metric_v2)

# Compare alignment rates

Improve Judge Quality

Iterate on judge prompts to improve alignment:

  1. Identify misalignment patterns
  2. Update judge prompt with clearer criteria
  3. Re-evaluate alignment
  4. Repeat until satisfactory

Next Steps