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LLM Testing & Evaluation

Stop shipping prompts on vibes. Winnow gives you eval suites, golden datasets, LLM-as-judge scoring, and automatic regression detection so every change to your AI pipeline is measured before it hits production.

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run_eval_suite.pypython

import winnow

# Define an eval suite with multiple scorers
suite = winnow.create_eval_suite(
    name="summarization-quality",
    dataset="golden-summaries-v3",
    scorers=[
        winnow.scorers.LLMJudge(
            criteria="faithfulness",
            model="gpt-4o",
            scale=(1, 5),
        ),
        winnow.scorers.LLMJudge(
            criteria="conciseness",
            model="gpt-4o",
            scale=(1, 5),
        ),
        winnow.scorers.ROUGE(variant="rouge-l"),
        winnow.scorers.Latency(),
        winnow.scorers.Cost(),
    ],
)

# Run the suite against a prompt + model combo
results = winnow.run_eval(
    suite_id=suite.id,
    model="claude-sonnet",
    prompt_version="v3.2",
    concurrency=20,
)

print(results.summary())
# faithfulness: 4.3 | conciseness: 4.1 | rouge-l: 0.72
# latency_p50: 410ms | cost_per_call: $0.0028
# regression vs baseline: none detected

Evaluate Every Dimension of LLM Quality

Build eval suites that catch regressions across quality, cost, latency, and safety before your users do.

Eval Suites

Compose reusable eval suites from dozens of built-in scorers. Run them on every commit, every deploy, or on a schedule. Results feed directly into experiment dashboards.

Golden Datasets

Curate versioned golden datasets with expected outputs. Winnow tracks dataset lineage so you always know which test cases were active for a given eval run.

LLM-as-Judge Scoring

Use frontier models as automated judges with configurable criteria, rubrics, and scoring scales. Calibrate judges against human annotations to measure agreement.

Regression Detection

Winnow automatically compares each eval run against your baseline. Get alerts when quality drops, latency spikes, or costs creep up beyond configurable thresholds.

Catch regressions before your users do

Traditional unit tests don't work for stochastic LLM outputs. Winnow combines deterministic checks, statistical comparisons, and LLM-as-judge evaluations into a single pipeline that runs in your CI or on a cron.

50+ built-in scorers: ROUGE, BLEU, BERTScore, faithfulness, toxicity

CI/CD integration with GitHub Actions, GitLab CI, and webhooks

Versioned golden datasets with automatic drift detection

Human annotation workflows to calibrate LLM judges

Historical trend charts for every metric across runs

Make every prompt change measurable

Create your first eval suite in under five minutes. Free for up to 5M agent runs per month.

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