> ## Documentation Index
> Fetch the complete documentation index at: https://docs.galileo.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# LLM spans without RAG

> Build a span-level Luna metric from multi-field examples without retrieved documents.

Use this tutorial when your metric depends on multiple text fields, but not on retrieved document context. A common example is instruction-following style evaluation where the model sees both an `input` and an `output`.

## Dataset schema

Typical columns:

* `input`: the source prompt, instruction, or user message
* `output`: the model response or candidate answer
* `label`: the ground-truth class for the metric

## Config shape

Use a span-style config with:

* `data_generation.metric.input_format: "tuple"`
* `data_generation.source_data.dataset.columns.features: ["input", "output"]`
* `training.metric.type: "boolean"` for binary metrics

## Minimal end-to-end config

```yaml theme={null}
run_steps:
  - data_generation
  - training

pipeline_provider: "local"
metric_name: "custom"

data_generation:
  metric:
    name: "Instruction Adherence"
    type: "binary"
    input_format: "tuple"
    llmaj_source_prompt: "Check if the response is consistent with the instructions."
  source_data:
    dataset:
      source_type: "huggingface"
      huggingface:
        name: "instruction_adherence_dataset"
  output:
    dataset:
      repo_name: "instruction-adherence-training"

training:
  dataset:
    name: "instruction-adherence-training"
  prompt_template: |
    Check if the response is consistent with the instructions.

    Input: {input}
    Response: {output}

    Respond with "true" or "false".
  output:
    model_name: "instruction-adherence-model"
```