Evals · Schema

Scorer

A function that takes (input, output, optional expected, optional context) and produces a score plus optional rationale. Scorers come in several flavors: code-based (deterministic), heuristic, reference-based (compared to ground truth), reference-free (criterion adherence on raw output), LLM-as-a-judge, pairwise, and human ratings. Every eval platform — Braintrust, LangSmith, Inspect AI, DeepEval, Weave, TruLens — exposes a scorer abstraction with this shape.

EvalsLLM EvaluationAI QualityBenchmarksLLM as a JudgeObservabilityAgent EvaluationRAG EvaluationTest-Driven AI

Properties

Name Type Description
id string
name string
description string
type string
implementation string Pointer to the implementation: a function reference, a judge prompt template, or a model identifier.
judge_model string When type is llm_judge, the model used to score.
judge_prompt string When type is llm_judge, the prompt template applied to score each case.
scale object Score range and step semantics.
threshold number Default pass/fail threshold for this scorer (if applicable).
rubric string Optional human-readable rubric describing how a perfect, partial, and failing answer look.
tags array
View JSON Schema on GitHub

JSON Schema

evals-scorer-schema.json Raw ↑ 
{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://raw.githubusercontent.com/api-evangelist/evals/refs/heads/main/json-schema/evals-scorer-schema.json",
  "title": "Scorer",
  "description": "A function that takes (input, output, optional expected, optional context) and produces a score plus optional rationale. Scorers come in several flavors: code-based (deterministic), heuristic, reference-based (compared to ground truth), reference-free (criterion adherence on raw output), LLM-as-a-judge, pairwise, and human ratings. Every eval platform — Braintrust, LangSmith, Inspect AI, DeepEval, Weave, TruLens — exposes a scorer abstraction with this shape.",
  "type": "object",
  "properties": {
    "id": {
      "type": "string",
      "example": "scorer_faithfulness_v2"
    },
    "name": {
      "type": "string",
      "example": "faithfulness"
    },
    "description": {
      "type": "string",
      "example": "Measures whether the answer is grounded in the retrieved context (RAG faithfulness)."
    },
    "type": {
      "type": "string",
      "enum": ["code", "llm_judge", "human", "heuristic", "reference_based", "reference_free", "pairwise"],
      "example": "llm_judge"
    },
    "implementation": {
      "type": "string",
      "description": "Pointer to the implementation: a function reference, a judge prompt template, or a model identifier.",
      "example": "python:my_evals.faithfulness:v2"
    },
    "judge_model": {
      "type": "string",
      "description": "When type is llm_judge, the model used to score.",
      "example": "claude-opus-4-7"
    },
    "judge_prompt": {
      "type": "string",
      "description": "When type is llm_judge, the prompt template applied to score each case."
    },
    "scale": {
      "type": "object",
      "description": "Score range and step semantics.",
      "properties": {
        "min": { "type": "number", "example": 0 },
        "max": { "type": "number", "example": 1 },
        "step": { "type": "number", "example": 0.01 },
        "kind": {
          "type": "string",
          "enum": ["continuous", "binary", "ordinal", "categorical"],
          "example": "continuous"
        }
      }
    },
    "threshold": {
      "type": "number",
      "description": "Default pass/fail threshold for this scorer (if applicable).",
      "example": 0.8
    },
    "rubric": {
      "type": "string",
      "description": "Optional human-readable rubric describing how a perfect, partial, and failing answer look.",
      "example": "1.0 = every claim in the answer is directly supported by the retrieved context. 0.5 = mostly supported with minor unsupported additions. 0.0 = answer contradicts or invents content."
    },
    "tags": {
      "type": "array",
      "items": { "type": "string" },
      "example": ["rag", "groundedness"]
    }
  },
  "required": ["id", "name", "type"]
}