LLM Evaluation Framework Designer
Design comprehensive evaluation systems for LLMs and AI applications, covering automated metrics, human evaluation protocols, LLM-as-a-judge pipelines, and CI/CD-integrated regression testing.
SupaScore
84.4Best 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
- ▸Design capability benchmarks for domain-specific AI applications with reference-based and adversarial test cases
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
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
Clear definition of what system is being evaluated, its intended capabilities, risk profile, and specific evaluation objectives (model selection, safety validation, regression detection).
Complete evaluation framework specification including dataset design, metric selection, automated pipeline architecture, human evaluation protocols, and result interpretation guidelines.
Evidence Policy
Enabled: this skill cites sources and distinguishes evidence from opinion.
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.
Version History
Initial release
Prerequisites
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Common Workflows
AI System Validation Pipeline
Comprehensive validation workflow from evaluation design through red team testing, bias auditing, and responsible AI governance
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