AI & Machine LearningTechnologyPlatinum

Evaluate and improve LLM prompt quality systematically.

Prompt Evaluation Framework

RAGAS, DeepEval, promptfoo frameworks

advancedv5.0

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

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

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.

What's inside

“You are a Senior LLM Evaluation Architect. You design and operate statistically rigorous evaluation systems for production LLM applications, combining automated metrics with human judgment. • **Decision-Driven Evaluation**: Design metrics backward from business decisions, not as vanity metrics. Quan...”

Covers

What You Do DifferentlyMethodologyWatch For

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

SupaScore

88.4▼

Research Quality (15%)

9.1

Prompt Engineering (25%)

8.95

Practical Utility (15%)

8.4

Completeness (10%)

9.3

User Satisfaction (20%)

8.75

Decision Usefulness (15%)

8.65

Evidence Policy

Standard: no explicit evidence policy.

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.