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Valiqor’s evaluation engine measures AI output quality using a combination of heuristic metrics (fast, deterministic) and LLM judge metrics (semantic, context-aware). This page explains what each metric measures, how scoring works, and how quality grades are assigned.

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Metrics Overview

Valiqor ships with 17 built-in metrics across two categories:

4 Heuristic Metrics

Fast, deterministic checks that don’t require an LLM. Ideal for format validation, exact matching, and string comparison.

13 LLM Judge Metrics

Semantic evaluation using an LLM as a judge. Used for quality, relevance, hallucination detection, and task adherence.

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Heuristic Metrics

These metrics run locally without any LLM calls. They return a score between 0.0 and 1.0:
MetricDescriptionScoring
containsChecks if the output contains expected substrings1.0 if all expected strings found, 0.0 otherwise
equalsExact string match between output and expected1.0 if exact match, 0.0 otherwise
regex_matchTests output against a regular expression pattern1.0 if pattern matches, 0.0 otherwise
levenshteinEdit distance between output and expected textNormalized 0.0–1.0 (1.0 = identical)
Usage example: 
result = client.eval.evaluate(
    dataset=[{
        "input": "What is 2+2?",
        "output": "The answer is 4",
        "expected": "4"
    }],
    metrics=["contains", "levenshtein"]
)

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LLM Judge Metrics

These metrics use an LLM to evaluate output quality semantically. Each metric sends a structured prompt to the judge model and receives a normalized score (0.0–1.0) with an explanation.

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Quality & Relevance

MetricWhat It Measures
answer_relevanceHow relevant the response is to the user’s question
coherenceLogical flow and consistency of the response
fluencyLanguage quality — grammar, readability, naturalness
response_completenessWhether all parts of the question are addressed
task_adherenceHow well the output follows specific instructions or constraints

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Factual Accuracy

MetricWhat It Measures
hallucinationWhether the output contains fabricated or unsupported claims
factual_accuracyWhether statements are factually correct

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RAG-Specific

MetricWhat It Measures
context_precisionHow much of the retrieved context is relevant to the query
context_recallWhether the retrieved context covers all information needed

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Specialized

MetricWhat It Measures
intent_resolutionWhether the correct user intent was identified and addressed
tool_call_accuracyWhether the right tools were called with correct arguments
moderationContent safety — flags harmful, toxic, or inappropriate content
retrievalQuality of the retrieval step (if applicable)

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How LLM Judging Works

Each LLM judge metric follows this flow: 
Input Data (prompt, response, context)
    ↓
Structured Evaluation Prompt
    ↓
LLM Judge (default: gpt-4o)
    ↓
Structured Output (score + explanation)
    ↓
Normalized Score (0.0 – 1.0)
  1. The metric receives the dataset item (input, output, context, expected)
  2. A structured prompt is constructed for the specific metric
  3. The prompt is sent to the judge LLM
  4. The LLM returns a structured response with a score and rationale
  5. The score is normalized to the 0.0–1.0 range
The judge model defaults to gpt-4o but can be configured. You can use your own OpenAI API key via the openai_api_key parameter — see BYOK for details.

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Score Results

Each metric evaluation produces a ScoreResult:
FieldTypeDescription
valuefloatScore (typically 0.0–1.0)
namestrMetric name
reasonstrLLM explanation for the score
metadatadictAdditional data (confidence, model used, etc.)
scoring_failedboolWhether computation failed
execution_time_msfloatTime taken to compute

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Aggregation & Quality Grades

When evaluating a dataset with multiple items and metrics, Valiqor computes aggregate scores:

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Aggregation

  • Per-metric average: Mean of all item scores for each metric
  • Overall quality: Mean of all per-metric averages

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Quality Grades

The overall quality score is mapped to a letter grade:
GradeScore RangeInterpretation
A≥ 0.9Excellent — system performing well
B≥ 0.8Good — minor improvements possible
C≥ 0.7Acceptable — meets minimum threshold
D≥ 0.6Below threshold — review recommended
F< 0.6Poor — significant issues detected
The default quality threshold is 0.7 (Grade C). Scores below this trigger insights and recommendations in the evaluation result.

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Metric Configuration

You can customize which metrics to run and configure them per-project: 
# Run specific metrics only
result = client.eval.evaluate(
    dataset=data,
    metrics=["hallucination", "answer_relevance", "task_adherence"]
)

# List available metric templates
templates = client.eval.list_metric_templates()

# Configure per-project metrics
client.eval.add_project_metric(
    project_name="my-chatbot",
    metric_key="hallucination"
)

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Quality Threshold

The default quality threshold is 0.7 (Grade C). Scores below this trigger insights and recommendations in the evaluation result. You can adjust this threshold per-project.

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Dataset Format

Evaluation datasets are lists of dictionaries. Required and optional fields depend on the metrics being used: 
dataset = [
    {
        "input": "What is the capital of France?",    # Required: user prompt
        "output": "The capital of France is Paris.",   # Required: AI response
        "context": ["France is a country in Europe. Its capital is Paris."],  # Optional: for RAG metrics
        "expected": "Paris"                            # Optional: for heuristic metrics
    }
]
FieldRequiredUsed By
inputYesAll metrics
outputYesAll metrics
contextFor RAG metricscontext_precision, context_recall, hallucination
expectedFor heuristic metricscontains, equals, regex_match, levenshtein

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Where Metrics Are Used

Evaluation metrics appear throughout Valiqor:
  • Standalone evaluations — evaluate() runs metrics on a dataset
  • Trace evaluations — evaluate_trace() extracts data from a trace and evaluates
  • Failure Analysis — FA uses metric scores as evidence for failure classification
  • Trends — get_trends() tracks metric scores over time
  • Comparisons — compare_runs() compares metrics across evaluation runs
See the Evaluations workflow for complete usage examples.