# `Arcana.Grounder.LLMJudge`
[🔗](https://github.com/georgeguimaraes/arcana/blob/main/lib/arcana/grounder/llm_judge.ex#L1)

LLM-as-judge grounder using atomic claim decomposition.

Asks an LLM to decompose the answer into atomic factual claims and
verify each claim against the retrieved chunks. Each claim comes back
with a verdict (`supported`, `unsupported`, or `contradicted`) and the
1-based indices of the chunks that support it. Claims are then mapped
back to byte offsets in the answer for span-level highlighting.

This follows the RAGAS faithfulness pattern: one structured-output LLM
call instead of token-level NLI scoring. Compared to
`Arcana.Grounder.Hallmark`, this grounder:

- Does a single LLM call regardless of answer length or chunk count.
- Uses semantic reasoning instead of token-overlap NLI, which handles
  paraphrase and synthesis better.
- Returns chunk attribution directly from the LLM rather than via a
  secondary word-overlap pass.

## Requirements

Requires `req_llm`. The judge model defaults to
`Application.get_env(:arcana, :judge_model)`, falling back to
`"anthropic:claude-haiku-4-5"`.

## Usage

    Pipeline.ground(ctx, grounder: Arcana.Grounder.LLMJudge)

    Loop.run(question, grounder: Arcana.Grounder.LLMJudge)

## Options

- `:judge_model` - Model spec passed to ReqLLM. Defaults to
  `Application.get_env(:arcana, :judge_model, "anthropic:claude-haiku-4-5")`.
- `:judge_temperature` - Sampling temperature. Defaults to `0.0` for
  deterministic verdicts.
- `:judge_max_tokens` - Max output tokens. Defaults to `2048`.
- `:judge_fn` - Override the LLM call entirely with a 3-arity function
  `(question, answer, chunks) -> {:ok, %{claims: [...]}} | {:error, term}`.
  Used for tests to avoid network calls.

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*Consult [api-reference.md](api-reference.md) for complete listing*