This comprehensive evaluation framework for Large Language Model (LLM) generative tasks provides systematic guidance for assessing output quality and gathering meaningful evaluation data. The framework is designed as a decision-tree style system that helps teams select appropriate metrics, evaluation approaches, and implementation strategies based on their specific use cases.
📊 Decision Trees for Metric Selection (Primary Authority)
Systematic guidance for selecting appropriate evaluation metrics based on:
- Task Type: Q&A, RAG, Creative Writing, Code Generation, Summarization
- Evaluation Context: Development, Production, Research
- Budget Constraints: Resource allocation guidelines
- Quality Requirements: Business impact assessment
Key Features:
- Prioritized metric selection paths
- Budget allocation guidelines
- Quick reference tables for common use cases
- Customization frameworks for domain-specific needs
Related: Quality Dimensions | Cost Calculator | Tool Matrix
Comprehensive mapping of quality dimensions with LLM-specific considerations:
Core Dimensions:
- Accuracy & Factualness: Hallucination detection, knowledge verification
- Relevance & Helpfulness: Intent alignment, task completion
- Safety & Harmlessness: Bias detection, content filtering
- Style & Coherence: Writing quality, logical consistency
- Instruction Following: Constraint adherence, format compliance
RAG-Specific Dimensions:
- Context Precision & Recall: Retrieval quality assessment
- Answer Faithfulness: Grounding in provided context
- Performance & Efficiency: Latency, cost optimization
Related: Decision Trees | Implementation Guides | Evaluation Wizard
Step-by-step implementation instructions for different evaluation approaches:
Automated Evaluation:
- Semantic metrics (BERTScore, semantic similarity)
- Linguistic metrics (readability, grammar)
- Task-specific metrics (ROUGE, BLEU)
- Cost analysis and optimization
LLM-as-Judge Evaluation:
- Multi-model judge frameworks
- Consensus building mechanisms
- Calibration and reliability testing
- Cost-effectiveness analysis
Human Evaluation:
- Annotation platform setup
- Quality control protocols
- Inter-rater reliability testing
- Performance monitoring
Hybrid Approaches:
- Multi-stage evaluation pipelines
- Adaptive evaluation strategies
- Production monitoring systems
Related: Tool Matrix | Master Roadmap | Starter Toolkit
Standardized guidelines and templates for human evaluation:
Evaluation Templates:
- Question Answering (QA-EVAL-001)
- RAG Systems (RAG-EVAL-001)
- Creative Writing (CW-EVAL-001)
- Code Generation (CODE-EVAL-001)
Quality Control:
- Inter-rater reliability protocols
- Golden standard creation
- Annotator training programs
- Performance monitoring systems
Related: Implementation Guides | Tool Matrix | Cost Calculator
Production-ready templates for automated evaluation systems:
Pipeline Architectures:
- Basic evaluation pipeline
- RAG evaluation pipeline
- Production monitoring pipeline
Deployment Options:
- Docker containerization
- Kubernetes deployment
- Environment-specific configurations
Monitoring & Alerting:
- Prometheus metrics
- Grafana dashboards
- Alert rule configurations
- Performance testing suites
Related: Implementation Guides | Tool Matrix | Master Roadmap
Use the Decision Tree to identify:
- Primary task type
- Quality requirements
- Available resources
- Evaluation timeline
Quick Start: Quick Assessment Tool for instant recommendations
Reference the Quality Mapping to:
- Prioritize relevant dimensions
- Set target benchmarks
- Understand LLM-specific considerations
Interactive Guide: Evaluation Selection Wizard for detailed guidance
Follow the Implementation Guide to:
- Select appropriate evaluation methods
- Understand cost implications
- Plan deployment strategy
Tool Selection: Tool Comparison Matrix for vendor and platform guidance
Use provided templates:
- Human evaluation templates for manual assessment
- Automation templates for scalable systems
- Starter Evaluation Toolkit for day 1 implementation
Strategic Planning: Master Implementation Roadmap for long-term planning
- Prioritizes metrics that directly impact business objectives
- Balances quality requirements with resource constraints
- Provides clear guidance for decision-making
Based on research showing:
- 85% agreement between LLM-as-judge and human evaluators
- 3.5X ROI improvement with strategic evaluation frameworks
- 60-80% cost reduction through hybrid approaches
- Production-ready code templates
- Deployment configurations
- Monitoring and alerting setups
- Quality control protocols
- Multiple evaluation approaches (automated, LLM-judge, human)
- Diverse task types (Q&A, RAG, creative, code)
- Various deployment contexts (development, production, research)
For detailed implementation guidance, see the Master Implementation Roadmap which provides four specialized templates:
- Startup MVP (0-6 months): Quick deployment for small teams
- Enterprise Rollout (0-12 months): Comprehensive enterprise implementation
- Research Project (0-9 months): Academic research methodology
- Emergency Response (0-2 weeks): Crisis resolution strategies
For comprehensive cost analysis, ROI calculations, and budget optimization guidance, see the Cost-Benefit Calculator which includes:
- Detailed cost breakdowns by evaluation approach and use case
- ROI calculators with industry benchmarks
- Budget allocation guidelines for different risk levels
- Use case-specific cost estimates and optimization strategies
This framework is built on comprehensive research from leading AI organizations:
- LLM Evaluation Methods: Analysis of 200+ evaluation papers (2023-2025)
- Metric Effectiveness: Correlation studies between automated and human assessment
- Cost-Benefit Analysis: ROI studies across different evaluation approaches
- OpenAI Evals: Community-driven evaluation frameworks
- Anthropic Constitutional AI: Safety-focused evaluation approaches
- Google Vertex AI: Multi-model evaluation systems
- Microsoft Azure AI: Lifecycle-integrated evaluation
- RAGAS: Reference-free RAG evaluation
- TruLens: LLM application monitoring
- G-Eval: Chain-of-thought evaluation
- QUEST: Structured human evaluation
Choose your entry point based on your experience and timeline:
Quick Assessment Tool: Instant recommendations based on your project characteristics
Evaluation Selection Wizard: Interactive guidance for selecting metrics and approaches
Starter Evaluation Toolkit: Day 1 implementation with code examples
Master Implementation Roadmap: Long-term planning with four specialized templates
This framework provides specialized guidance for four primary AI use cases:
| Use Case | Primary Focus | Key Metrics | Budget Range |
|---|---|---|---|
| Customer Support | User satisfaction, accuracy | Accuracy, Relevance, Safety | $2,000-4,000/month |
| Content Creation | Creativity, brand alignment | Creativity, Coherence, Style | $3,000-6,000/month |
| Document Q&A (RAG) | Information accuracy, grounding | Faithfulness, Accuracy, Citations | $2,200-4,500/month |
| Code Generation | Functional correctness, security | Execution, Correctness, Security | $1,500-3,000/month |
For detailed guidance on each use case, see the Decision Trees for Metric Selection.
This framework represents the current state-of-the-art in LLM evaluation. As the field evolves, we encourage:
- Feedback: Share experiences and suggestions for improvement
- Contributions: Add new metrics, templates, or use case examples
- Adaptation: Customize frameworks for specific domains or applications
- Research: Contribute findings on evaluation effectiveness and best practices
The goal is to provide a living framework that evolves with the rapidly advancing field of LLM evaluation, ensuring teams can build reliable, high-quality AI systems that serve users effectively and safely.