AI Evaluation Framework Builder

Designs comprehensive evaluation frameworks for LLM and AI systems, combining automated metrics (BLEU, ROUGE, BERTScore), LLM-as-judge patterns (G-Eval), RAG evaluation (RAGAS), standard benchmarks (MMLU, HumanEval), safety evaluations, and A/B testing methodologies into production-ready evaluation pipelines.

Gold

v1.0.00 activationsAI & Machine LearningTechnologyexpert

SupaScore

84.9

Research Quality (15%)

8.5

Prompt Engineering (25%)

8.7

Practical Utility (15%)

8.5

Completeness (10%)

8.5

User Satisfaction (20%)

8.2

Decision Usefulness (15%)

8.5

Best for

▸Building production evaluation pipelines for RAG systems combining RAGAS faithfulness with LLM-as-judge relevance scoring
▸Designing A/B testing frameworks to compare GPT-4 vs Claude performance on customer support tasks with automated BLEU/ROUGE baselines
▸Creating safety evaluation suites for financial AI assistants using G-Eval combined with hallucination detection and regulatory compliance checks
▸Implementing continuous evaluation monitoring for code generation models using HumanEval benchmarks with custom business logic validation
▸Architecting multi-metric evaluation dashboards that correlate BERTScore semantic similarity with human preference ratings for content generation

What you'll get

●Multi-stage evaluation architecture with automated ROUGE baselines feeding into G-Eval semantic scoring, including cost breakdowns and statistical significance thresholds
●Production-ready Python evaluation pipeline with RAGAS faithfulness, custom safety classifiers, and Weights & Biases experiment tracking integration
●Comprehensive evaluation strategy document mapping business requirements to specific metrics (BERTScore for semantic similarity, HumanEval for code quality) with A/B testing protocols

Not designed for ↓

×Training or fine-tuning the AI models themselves - this focuses purely on evaluation methodology
×Building the underlying ML infrastructure or model serving systems
×Creating the datasets or ground truth data that evaluations run against
×Designing user interfaces for evaluation results display

Expects

Clear specification of the AI system type (RAG, chatbot, agent, etc.), target quality dimensions (accuracy, safety, latency), risk tolerance level, and evaluation budget constraints.

Returns

Complete evaluation framework architecture with metric selection rationale, implementation code samples, cost estimates, and production deployment guidelines.

Evidence Policy

Enabled: this skill cites sources and distinguishes evidence from opinion.

llm-evaluationai-benchmarksbleu-rougebertscorellm-as-judgeg-evalragasrag-evaluationmmluhuman-evala-b-testingsafety-evaluationhallucination-detectiondeepeval

Research Foundation: 9 sources (4 official docs, 4 paper, 1 industry frameworks)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.

Version History

v1.0.02/15/2026

Initial release

Works well with

A/B Test AnalystPlatinum AI Red Teaming SpecialistGold LLM Observability EngineerGold ML Experiment TrackerGold Prompt Evaluation FrameworkGold

Need more depth?

Specialist skills that go deeper in areas this skill touches.

AI Guardrails EngineerGold Responsible AI Framework BuilderPlatinum Drift Monitoring Pipeline DesignerGold

Common Workflows

AI Safety Evaluation Pipeline

Comprehensive safety evaluation starting with framework design, followed by adversarial testing, and ending with production guardrails implementation

ai-evaluation-framework-builder→AI Red Teaming Specialist→AI Guardrails Engineer

Production AI Quality Assurance

End-to-end quality assurance from initial evaluation design through production monitoring and performance drift detection

ai-evaluation-framework-builder→LLM Observability Engineer→Drift Monitoring Pipeline Designer

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