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

# LLM spans with RAG

> Train a document-grounded Luna evaluator using retrieved context.

Use this tutorial when your evaluator depends on retrieved documents as context. This is the standard RAG pattern for Luna evaluators such as context adherence.

## Dataset schema

Minimum columns:

* `documents`: the retrieved context
* at least one of:
  * `input`: the user question
  * `output`: the model answer
* `label`: the ground-truth class for the evaluator

## Config shape

Set:

* `data_generation.metric.input_format: "rag"`
* `data_generation.source_data.dataset.columns.features` to a subset of `["documents", "input", "output"]`
* `generation.context_examples: 1`

## Minimal end-to-end config

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

pipeline_provider: "local"
metric_name: "custom"

data_generation:
  metric:
    name: "Context Adherence"
    type: "binary"
    input_format: "rag"
    llmaj_source_prompt: "Determine whether the answer is consistent with the retrieved documents."
  source_data:
    dataset:
      source_type: "huggingface"
      huggingface:
        name: "context_adherence_dataset"
  generation:
    context_examples: 1
  output:
    dataset:
      repo_name: "context-adherence-training"

training:
  dataset:
    name: "context-adherence-training"
  prompt_template: |
    Determine whether the answer is consistent with the retrieved documents.
    Question:
    {input}

    Documents:
    {documents}

    Answer:
    {output}

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