AI & Machine LearningTechnologyPlatinum

Design evaluation systems for AI models and applications.

LLM Evaluation Framework Designer

Evaluation Metrics, Human Protocols, CI/CD

expertv5.0

Best for

▸Design comprehensive evaluation suites for production RAG systems including faithfulness and hallucination detection
▸Build LLM-as-a-judge pipelines with bias mitigation for automated scoring of open-ended generation tasks
▸Create regression testing frameworks for model updates with contamination-resistant datasets and CI/CD integration
▸Establish human evaluation protocols with inter-annotator agreement metrics for safety and alignment testing

What you'll get

▸Detailed evaluation framework specification with capability taxonomy, dataset design rationale, metric selection matrix, and implementation architecture diagrams
▸Complete human evaluation protocol with annotator guidelines, inter-rater reliability procedures, and statistical analysis plans
▸LLM-as-a-judge pipeline design with bias mitigation strategies, prompt templates, and calibration procedures

Expects

Clear definition of what system is being evaluated, its intended capabilities, risk profile, and specific evaluation objectives (model selection, safety validation, regression detection).

Returns

Complete evaluation framework specification including dataset design, metric selection, automated pipeline architecture, human evaluation protocols, and result interpretation guidelines.

What's inside

“You are an LLM Evaluation Framework Designer. You synthesize methodologies from HELM, Chatbot Arena, OpenAI's Evals, DeepEval, RAGAS, and MT-Bench to design comprehensive evaluation systems for language models, RAG applications, and agentic systems, understanding fundamental tradeoffs between automa...”

Covers

What You Do DifferentlyMethodologyWatch For

Not designed for ↓

×Training or fine-tuning language models themselves
×Building the actual AI applications being evaluated
×Generating synthetic training data for model improvement
×Implementing monitoring solutions for production systems

SupaScore

87.5▼

Research Quality (15%)

9.25

Prompt Engineering (25%)

8.75

Practical Utility (15%)

8.25

Completeness (10%)

9.25

User Satisfaction (20%)

8.5

Decision Usefulness (15%)

8.75

Evidence Policy

Standard: no explicit evidence policy.

llm-evaluationai-testingbenchmark-designllm-as-judgehuman-evaluationregression-testingevaluation-metricsrag-evaluationmodel-comparisonai-safety-testingml-opsautomated-scoring

Research Foundation: 8 sources (5 academic, 2 official docs, 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.