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When to use this flow

Use labelling-only mode when you already have training examples, but they are not labelled for the metric you want to train. This flow uses your LLM-as-a-Judge prompt to label the dataset you already have. It does not generate new synthetic examples.

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What changes in the config

To enable this flow, set:
  • labelling.label_only_mode: true
  • metric.llmaj_source_prompt to the prompt used for judging / labeling
If you are using Huggingface input, your dataset should contain a train split If you are using CSV input, you must also set:
  • source_data.dataset.csv.train_file_path

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Minimal example

data_generation:
  metric:
    llmaj_source_prompt: |
      ...
  source_data:
    dataset:
      source_type: "csv"
      csv:
        file_path: "./test.csv"
        train_file_path: "./train.csv"
      columns:
        features: ["input"]
        label: "label"
  labelling:
    enabled: false
    label_only_mode: true

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What happens next

After labeling completes, continue to the training step using the labeled dataset. Next: Training overview