LLM Evaluation Framework

LLM Evaluation Framework

Phase 1: Evaluation Criteria Definition

1. Define use case requirements
   • Task type (generation, classification, extraction, summarization, Q&A)
   • Input characteristics (length, language, domain)
   • Output requirements (format, length, structure)
   • Latency requirements (real-time vs. batch)
   • Throughput requirements (requests per minute)
   • Cost constraints (per-token or per-request budget)
   • Privacy and data residency requirements
   • Safety and content policy requirements
2. Define evaluation metrics and weights

Evaluation Criteria Weights

Phase 2: Evaluation Dataset Creation

1. Build evaluation datasets
   • Representative inputs from real use case
   • Edge cases and adversarial inputs
   • Multi-language inputs (if applicable)
   • Long-context inputs (if applicable)
   • Ground truth / reference outputs for automated scoring
   • Minimum 100+ examples per task category
2. Create human evaluation rubrics
3. Define automated evaluation metrics (BLEU, ROUGE, exact match, F1)

Dataset Summary

Phase 3: Quality Evaluation

1. Run task-specific quality benchmarks
   • Generate outputs for all evaluation examples
   • Score with automated metrics
   • Conduct human evaluation on sample
   • Evaluate instruction following accuracy
   • Test structured output generation (JSON, XML)
   • Assess factual accuracy and hallucination rate
2. Compare across models

Quality Comparison

Phase 4: Performance & Cost Evaluation

1. Measure performance characteristics
   • Time to first token (TTFT)
   • Tokens per second (throughput)
   • P50, P95, P99 end-to-end latency
   • Latency under concurrent load
   • Rate limit behavior
   • Context window utilization impact on latency
2. Calculate cost per use case
   • Input token cost
   • Output token cost
   • Total cost per request (average)
   • Monthly projected cost at expected volume

Performance & Cost Comparison

Phase 5: Safety & Reliability Evaluation

1. Test safety and content policies
   • Harmful content generation resistance
   • PII handling and redaction
   • Bias in outputs across demographics
   • Jailbreak resistance
   • Content policy compliance
   • Confidentiality (does not leak training data)
2. Test reliability
   • API uptime and error rates
   • Consistency of outputs (temperature=0 determinism)
   • Graceful degradation under load
   • Rate limit recovery behavior

Phase 6: Selection & Recommendation

1. Score each model across all criteria
2. Apply weights to calculate overall score
3. Consider operational factors (vendor relationship, support, SLA)
4. Evaluate fine-tuning potential if needed
5. Make recommendation with justification

Final Comparison Matrix

Counter-Rationalizations

Output Format

• Evaluation Dataset: Test cases with ground truth
• Quality Report: Per-model quality metrics and human eval results
• Performance Report: Latency and throughput benchmarks
• Cost Analysis: Per-model cost projections at expected volume
• Recommendation: Selected model with justification and tradeoffs

Action Items

• Define evaluation criteria and weights for use case
• Build evaluation dataset with representative examples
• Run quality benchmarks across candidate models
• Measure performance and calculate costs
• Conduct safety and reliability testing
• Select model and document decision rationale
• Set up monitoring for model quality in production