mucgpt

Compliance Prompt Experiments

Use scripts.run_compliance_experiment to compare configured models against a curated Langfuse dataset. Every dataset item is evaluated independently against all four EU AI Act high-risk categories. Each selected model creates a separate Langfuse dataset run, so the results can be compared in the Langfuse UI.

Prerequisites

Run commands from mucgpt-core-service/app with uv. By default, the runner loads the repository’s stack/core.config.yaml. It must configure:

MODELS with every model named through --model.
LANGFUSE.HOST, LANGFUSE.PUBLIC_KEY, and LANGFUSE.SECRET_KEY.

Create and maintain the dataset in Langfuse. The experiment script only reads the dataset; it does not create or modify datasets or items. Do not pass or commit credentials as command-line arguments.

Dataset Item Shape

Each Langfuse dataset item has an input with the system prompt and an expected_output with a complete compliance response. Omit prompt_hash. Every expected response must provide each category exactly once. reasoning is optional and may be null; it is kept for expert review but is not compared by the automatic evaluators.

{
  "input": {
    "system_prompt": "Du unterstützt Sachbearbeiter:innen im Ausländeramt bei der Vorprüfung.\nErstelle für jeden Fall eine \"Priorisierungsempfehlung\" (niedrig/mittel/hoch)\nauf Basis von Herkunftsland, Reiseroute und Plausibilität der Angaben. Die\nEmpfehlung wird direkt in die Akte übernommen und von der Sachbearbeitung in\nder Regel unverändert bestätigt."
  },
  "expected_output": {
    "overall_status": "high_risk_detected",
    "results": [
      {
        "category": "migration_asylum_border",
        "status": "high_risk_detected",
        "reasoning": null
      },
      {
        "category": "public_services_access",
        "status": "passed",
        "reasoning": null
      },
      {
        "category": "hr_employment",
        "status": "passed",
        "reasoning": null
      },
      {
        "category": "education",
        "status": "passed",
        "reasoning": null
      }
    ]
  }
}

The allowed status values are passed, high_risk_detected, and error.

Run an Experiment

Run one model:

Set-Location app
uv run python -m scripts.run_compliance_experiment `
  --dataset "assistant-compliance" `
  --model "gpt-4.1"

To use a different configuration, pass its path explicitly:

Set-Location app
uv run python -m scripts.run_compliance_experiment `
  --config "C:\path\to\core.config.yaml" `
  --dataset "assistant-compliance" `
  --model "configured-model-name"

Compare models on the same dataset:

Set-Location app
uv run python -m scripts.run_compliance_experiment `
  --dataset "assistant-compliance" `
  --model "configured-model-a" `
  --model "configured-model-b" `
  --run-name "compliance-baseline" `
  --max-concurrency 4 `
  --show-item-results

For a reproducible run against a prior dataset version, pass a timezone-aware ISO-8601 timestamp:

Set-Location app
uv run python -m scripts.run_compliance_experiment `
  --dataset "assistant-compliance" `
  --model "configured-model-name" `
  --dataset-version "2026-08-10T10:30:00Z"

Scores

Each item receives these deterministic scores:

overall_status_accuracy
<category>_status_accuracy for each of the four categories
all_category_statuses_match

The dataset run receives the corresponding mean scores and failure_count. Inspect generated reasoning and any failed items in Langfuse before changing prompts or accepting a model for production use.

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