ai-evaluation

name	ai-evaluation
description	Use when designing an evaluation framework for AI/LLM features. Covers golden dataset creation, automated scoring rubrics, hallucination detection, regression testing infrastructure, and production monitoring. Do not use for prompt design (use prompt-engineering) or RAG pipeline architecture (use rag-architecture).
metadata	{"author":"dtsong"}

AI Evaluation

Purpose

Design an evaluation framework for AI/LLM features, including golden dataset creation, automated scoring rubrics, hallucination detection, and regression testing infrastructure.

Scope Constraints

Reads feature specifications, evaluation requirements, and existing infrastructure details for framework design. Does not execute model inference, create production datasets, or access live model endpoints directly.

Inputs

• AI feature being evaluated (what it does, expected behavior)
• Input data examples and edge cases
• Quality requirements (accuracy thresholds, hallucination tolerance)
• Existing evaluation infrastructure (if any)
• Production monitoring requirements

Input Sanitization

No user-provided values are used in commands or file paths. All inputs are treated as read-only analysis targets.

Procedure

Progress Checklist

• Step 1: Define evaluation dimensions
• Step 2: Build golden dataset
• Step 3: Design automated scoring
• Step 4: Design hallucination detection
• Step 5: Design regression testing
• Step 6: Design production monitoring

Step 1: Define Evaluation Dimensions

Identify what "good" means for this feature:

• Correctness: Does the output match the expected answer?
• Faithfulness: Does the output only use information from the provided context?
• Relevance: Does the output answer the actual question asked?
• Completeness: Does the output cover all aspects of the question?
• Format compliance: Does the output match the expected structure?
• Safety: Does the output avoid harmful, biased, or inappropriate content?

Step 2: Build Golden Dataset

Create a high-quality evaluation dataset:

• Size: Minimum 50 examples, ideally 200+ for statistical significance
• Distribution: Cover common cases (60%), edge cases (25%), adversarial cases (15%)
• Labeling: Each example has input, expected output, and scoring criteria
• Source: Real user queries (anonymized) + synthetically generated edge cases
• Versioning: Dataset is version-controlled alongside the code

Step 3: Design Automated Scoring

Create scoring rubrics that can run without human review:

• Exact match: For classification, extraction, or structured output (score: 0 or 1)
• Semantic similarity: Embedding-based comparison of generated vs expected (score: 0-1)
• LLM-as-judge: Use a stronger model to evaluate the output (score: 1-5 rubric)
• Rule-based checks: Required fields present, format valid, no PII leaked
• Composite score: Weighted combination of individual dimensions

Step 4: Design Hallucination Detection

Build specific checks for fabricated content:

• Reference validation: Every cited fact must trace back to a source document
• Entity verification: Named entities (people, dates, numbers) must appear in context
• Confidence calibration: When the model says "I'm not sure," is it actually uncertain?
• Contradiction detection: Does the output contradict the provided context?
• Fabrication patterns: Common hallucination patterns to flag (fake URLs, invented citations)

Step 5: Design Regression Testing

Build a CI/CD-compatible evaluation pipeline:

• Trigger: Run on prompt changes, model upgrades, or code changes affecting AI features
• Threshold enforcement: Fail the build if eval score drops below threshold
• Comparison reporting: Show score delta vs previous version, highlight regressions
• Fast vs full: Quick smoke test (20 examples) for every commit, full eval (200+) for releases

Step 6: Design Production Monitoring

Plan ongoing quality monitoring:

• Sampling: Evaluate X% of production requests against automated scoring
• Feedback loop: User thumbs-up/down, explicit corrections
• Drift detection: Score distribution shift over time (model degradation, data drift)
• Alerting: Score drops below threshold, hallucination rate spikes, latency increases

Compaction resilience: If context was lost during a long session, re-read the Inputs section to reconstruct what system is being analyzed, check the Progress Checklist for completed steps, then resume from the earliest incomplete step.

Output Format

# AI Evaluation Framework

## Evaluation Dimensions
| Dimension | Weight | Scoring Method | Threshold |
|-----------|--------|---------------|-----------|
| Correctness | 40% | LLM-as-judge (1-5) | ≥ 4.0 |
| Faithfulness | 30% | Reference validation | ≥ 95% |
| Relevance | 20% | Semantic similarity | ≥ 0.85 |
| Format compliance | 10% | Rule-based | 100% |

## Golden Dataset
**Size:** [N examples]
**Distribution:**
| Category | Count | Description |
|----------|-------|-------------|
| Common cases | N | [Description] |
| Edge cases | N | [Description] |
| Adversarial | N | [Description] |

**Storage:** [Location in repo]
**Versioning:** [Approach]

## Scoring Rubric
### Correctness (LLM-as-Judge)
| Score | Criteria |
|-------|---------|
| 5 | Perfect — matches expected output in all aspects |
| 4 | Good — minor differences that don't affect usefulness |
| 3 | Acceptable — correct core answer with some issues |
| 2 | Poor — partially correct but missing key information |
| 1 | Wrong — incorrect or misleading answer |

## Hallucination Detection
| Check | Method | Severity |
|-------|--------|----------|
| Reference validation | [Approach] | Critical |
| Entity verification | [Approach] | High |
| Contradiction detection | [Approach] | High |

## Regression Testing Pipeline

[Code change] → [Smoke test (20 examples)] → [Pass?] → [Merge] [Release] → [Full eval (200+ examples)] → [Pass threshold?] → [Deploy]

**Threshold:** Composite score ≥ [X] to pass
**Reporting:** [Where results are published]

## Production Monitoring
| Metric | Sample Rate | Alert Threshold |
|--------|------------|-----------------|
| Composite score | 5% of requests | < [X] |
| Hallucination rate | 5% of requests | > [X%] |
| User satisfaction | All feedback | < [X] thumbs-up rate |

Handoff

• Hand off to prompt-engineering if evaluation results reveal prompt design deficiencies requiring structured redesign.
• Hand off to rag-architecture if evaluation findings indicate retrieval quality or chunking strategy issues.

Quality Checks

• Golden dataset has at least 50 examples covering common and edge cases
• Scoring rubric has clear criteria for each score level (not subjective)
• Hallucination detection checks references against source documents
• Regression testing is automated and blocks deploys on score drops
• Production monitoring includes both automated scoring and user feedback
• Eval dataset is version-controlled alongside the code

Evolution Notes

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