Prompt Evaluation Framework

Designs comprehensive LLM prompt evaluation frameworks with tailored metric suites, automated eval pipelines, A/B testing methodology, red-teaming protocols, and regression detection systems using industry-standard tools like RAGAS, DeepEval, and promptfoo.

Gold

v1.0.00 activationsAI & Machine LearningTechnologyadvanced

SupaScore

84.9

Research Quality (15%)

8.5

Prompt Engineering (25%)

8.5

Practical Utility (15%)

8.5

Completeness (10%)

User Satisfaction (20%)

8.2

Decision Usefulness (15%)

8.5

Best for

▸Designing comprehensive LLM prompt evaluation frameworks for production RAG systems with faithfulness and context relevance metrics
▸Setting up automated evaluation pipelines using RAGAS, DeepEval, or promptfoo for continuous prompt quality monitoring
▸Creating A/B testing methodology for comparing prompt variants with proper statistical significance testing
▸Building red-teaming protocols to test LLM robustness against adversarial inputs and prompt injections
▸Implementing regression detection systems to catch prompt quality degradation across model updates

What you'll get

●Multi-tiered evaluation framework specification with RAGAS faithfulness metrics, custom domain rubrics, and promptfoo CI/CD integration architecture
●Statistical A/B testing protocol with power analysis calculations, Wilcoxon signed-rank test methodology, and significance thresholds for prompt comparison
●Comprehensive test suite design covering happy path scenarios, edge cases, adversarial inputs, and regression anchor points with scoring criteria

Not designed for ↓

×Training or fine-tuning LLM models themselves - this focuses on evaluating existing model outputs
×Writing individual prompts or prompt engineering - this is about systematic evaluation of prompts
×General software testing without LLM-specific considerations
×Model performance benchmarking on standard datasets like GLUE or SuperGLUE

Expects

Clear description of the LLM application type (RAG, agent, chatbot, etc.), evaluation goals, risk tolerance, and existing infrastructure constraints.

Returns

Detailed evaluation framework specification including metric selection rationale, technical implementation architecture, test case design, and statistical analysis methodology.

Evidence Policy

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

llm-evaluationprompt-testingragasdeepevalpromptfooevaluation-metricsa-b-testingred-teamingregression-detectionbenchmark-designfaithfulnessllm-as-a-judgeeval-pipelineprompt-quality

Research Foundation: 8 sources (4 academic, 3 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

v1.0.02/14/2026

Initial release

Prerequisites

Use these skills first for best results.

Prompt Engineering StrategistGold

Works well with

AI Red Teaming SpecialistGold LLM Fine-Tuning StrategistGold LLM Observability EngineerGold Prompt Engineering StrategistGold RAG Architecture DesignerGold

Need more depth?

Specialist skills that go deeper in areas this skill touches.

AI Red Teaming SpecialistGold LLM Observability EngineerGold

Common Workflows

RAG System Quality Assurance

Complete RAG system development from architecture through prompt optimization to production monitoring

RAG Architecture Designer→Prompt Engineering Strategist→prompt-evaluation-framework→LLM Observability Engineer

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