> ## Documentation Index
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---

# Standard Quality Metrics

This page shows how to express widely-used LLM evaluation methods with Pydantic Evals primitives:

-   [`GEval`](/docs/ai/api/pydantic_evals/evaluators/#pydantic_evals.evaluators.GEval) -- a first-class evaluator implementing G-Eval chain-of-thought scoring (Liu et al., 2023).
-   Ready-made [`LLMJudge`](/docs/ai/api/pydantic_evals/evaluators/#pydantic_evals.evaluators.LLMJudge) rubrics for the RAG metrics popularized by [Ragas](https://github.com/explodinggradients/ragas) (faithfulness, answer relevance, context precision, context recall) and for GEMBA translation quality (Kocmi & Federmann, 2023).

The RAG and GEMBA metrics are provided as _rubric recipes_ rather than evaluator classes: each is one rubric away from `LLMJudge`, and a rubric you own adapts freely to your dataset shape and domain -- rename a field, tighten a criterion, or translate the instructions without waiting on a library release. Copy them into your project and edit as needed.

Rubric approximations, not the upstream implementations

These rubrics approximate each metric with a single LLM-judge call; they do not reproduce the upstream algorithms (for example, Ragas's `answer_relevancy` generates questions from the answer and compares embeddings). If you need parity with published numbers, wrap the real library as shown in [Third-Party Integrations](/docs/ai/evals/evaluators/framework-integrations/).

## G-Eval

[`GEval`](/docs/ai/api/pydantic_evals/evaluators/#pydantic_evals.evaluators.GEval) implements chain-of-thought evaluation: you provide the aspect being evaluated (`criteria`) and a list of explicit `evaluation_steps`, and the judge returns a reasoning trace plus an integer score in `score_range` (inclusive). Because the criteria and steps are user-supplied, `GEval` puts no structural requirements on the inputs, and it works in serialized datasets out of the box.

```python
from pydantic_evals import Case, Dataset
from pydantic_evals.evaluators import GEval

dataset = Dataset(
    name='g_eval_demo',
    cases=[Case(inputs='Explain how black holes form.')],
    evaluators=[
        GEval(
            criteria='coherence',
            evaluation_steps=[
                'Read the output carefully.',
                'Check that each sentence follows logically from the previous one.',
                'Assign a score from 1 (incoherent) to 5 (fully coherent).',
            ],
            include_input=True,
        ),
    ],
)
```

The result is an [`EvaluationReason`](/docs/ai/api/pydantic_evals/evaluators/#pydantic_evals.evaluators.EvaluationReason) whose value is the raw integer score -- on the scale you chose via `score_range`, not normalized to `0.0`\-`1.0` like [`LLMJudge`](/docs/ai/api/pydantic_evals/evaluators/#pydantic_evals.evaluators.LLMJudge) scores. If the judge returns a score outside `score_range`, the evaluation fails rather than recording a misleading value.

Simplified G-Eval

The published G-Eval method computes a probability-weighted expectation over score tokens using the judge model's log-probs. Pydantic Evals asks the model for a direct integer score instead, trading a small amount of correlation with human judgment for provider-agnostic simplicity. See Liu et al., 2023, "G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment".

## RAG metric rubrics

These recipes assume each case's `inputs` carries the user question and the context passages the output is supposed to rely on -- a _supplied_ context, not whatever an agent retrieved at runtime. With `include_input=True`, [`LLMJudge`](/docs/ai/api/pydantic_evals/evaluators/#pydantic_evals.evaluators.LLMJudge) shows the judge your full inputs object, so any shape works as long as the rubric describes it; adjust the wording if your fields are named differently.

```python
from dataclasses import dataclass

from pydantic_evals import Case, Dataset
from pydantic_evals.evaluators import LLMJudge

faithfulness = LLMJudge(
    rubric=(
        'Every factual claim in the Output must be directly supported by the context passages '
        'in the Input. Unsupported claims, contradictions, and fabrications constitute failure; '
        'ignore claims that are true in the real world but absent from the provided context. '
        'The score is the fraction of claims that are supported (0.0 = none, 1.0 = all); '
        'pass only if every claim is supported.'
    ),
    include_input=True,
    score={'evaluation_name': 'faithfulness'},
    assertion=False,
)

answer_relevance = LLMJudge(
    rubric=(
        'Judge whether the Output directly and completely answers the question in the Input, '
        'without padding or unrelated tangents. '
        'The score reflects how directly the Output addresses the question '
        '(0.0 = unrelated, 1.0 = a direct, on-point answer).'
    ),
    include_input=True,
    score={'evaluation_name': 'answer_relevance'},
    assertion=False,
)

context_precision = LLMJudge(
    rubric=(
        'This metric judges the retrieval, not the answer: assess the context passages in the '
        'Input against the question in the Input, and disregard the Output. '
        'The score is the fraction of the context that is relevant to answering the question '
        '(0.0 = none is relevant, 1.0 = all of it is relevant).'
    ),
    include_input=True,
    score={'evaluation_name': 'context_precision'},
    assertion=False,
)

context_recall = LLMJudge(
    rubric=(
        'This metric judges the retrieval, not the answer: determine whether the context '
        'passages in the Input contain enough information to produce the ground-truth answer '
        'in the Expected Output, and disregard the Output. '
        'The score is the fraction of the ground-truth answer that is supported by the context '
        '(0.0 = none of it, 1.0 = all of it).'
    ),
    include_input=True,
    include_expected_output=True,
    score={'evaluation_name': 'context_recall'},
    assertion=False,
)


@dataclass
class RagInputs:
    question: str
    context: list[str]


dataset = Dataset(
    name='rag_quality',
    cases=[
        Case(
            inputs=RagInputs(
                question='Where is the Eiffel Tower?',
                context=['The Eiffel Tower is in Paris, France.'],
            ),
            expected_output='The Eiffel Tower is in Paris.',
        ),
    ],
    evaluators=[faithfulness, answer_relevance, context_precision, context_recall],
)
```

Each recipe emits a `0.0`\-`1.0` score named via the `score` [`OutputConfig`](/docs/ai/api/pydantic_evals/evaluators/#pydantic_evals.evaluators.OutputConfig); swap `assertion=False` for an `assertion` config (or keep both) if you also want a pass/fail column, as described in [LLM Judge](/docs/ai/evals/evaluators/llm-judge/).

## GEMBA translation quality

The GEMBA Direct Assessment prompt (Kocmi & Federmann, 2023, "Large Language Models Are State-of-the-Art Evaluators of Translation Quality") scores a translation from 0 to 100. Here the case's `inputs` is the source text, the output is the candidate translation, and (optionally) the `expected_output` is a human reference translation:

```python
from pydantic_evals import Case, Dataset
from pydantic_evals.evaluators import LLMJudge

gemba_da = LLMJudge(
    rubric=(
        'The Input is the English source text and the Output is its French translation '
        '(the Expected Output, if present, is a human reference translation). '
        'Score the translation on a continuous scale from 0 to 100, where 0 means '
        '"no meaning preserved" and 100 means "perfect meaning and grammar", '
        'then report it normalized to the 0.0-1.0 range by dividing by 100.'
    ),
    include_input=True,
    include_expected_output=True,
    score={'evaluation_name': 'gemba_da'},
    assertion=False,
)

dataset = Dataset(
    name='translation_quality',
    cases=[
        Case(
            inputs='Hello, world!',
            expected_output='Bonjour, le monde !',
        ),
    ],
    evaluators=[gemba_da],
)
```

Adjust the language names to your language pair. For the GEMBA-SQM variant, replace the scale sentence with the anchored 0-6 scale from the paper (0 = no meaning preserved, 2 = some meaning preserved, 4 = most meaning preserved with few grammar mistakes, 6 = perfect meaning and grammar).

## Picking the right tool

Need

Use

Score a quality dimension on an integer scale with explicit CoT steps

[`GEval`](/docs/ai/api/pydantic_evals/evaluators/#pydantic_evals.evaluators.GEval)

Grounding, relevance, retrieval quality, translation quality

The [`LLMJudge`](/docs/ai/api/pydantic_evals/evaluators/#pydantic_evals.evaluators.LLMJudge) recipes above

Something bespoke

[`LLMJudge`](/docs/ai/api/pydantic_evals/evaluators/#pydantic_evals.evaluators.LLMJudge) with your own rubric

Exact parity with an upstream framework

[Third-Party Integrations](/docs/ai/evals/evaluators/framework-integrations/)