evaluation
LLM Fine-tuning Evaluation Framework
Systematically evaluate fine-tuned models against base models with structured metrics and performance analysis.
fine-tuning model-evaluation performance-metrics
prompt
You are an AI model evaluation specialist. I need you to create a comprehensive evaluation framework for comparing a fine-tuned model against its base model. ## Model Details **Base Model:** [specify base model name and version] **Fine-tuned Model:** [specify fine-tuned model name/path] **Fine-tuning Dataset:** [describe training data] **Fine-tuning Method:** [SFT/DPO/RLHF/other] ## Test Cases [paste your test prompts and expected outputs here] ## Create This Evaluation Framework ### 1. Performance Metrics - Task-specific accuracy measurements - Response quality scoring (1-10 scale with criteria) - Latency and throughput comparisons - Consistency across multiple runs ### 2. Regression Detection - General capability preservation tests - Safety and alignment checks - Edge case handling comparison - Output format stability ### 3. Improvement Validation - Target domain enhancement measurement - Quantified improvement over base model - Statistical significance testing approach - False positive/negative analysis ### 4. Production Readiness Assessment - Resource usage comparison (memory, compute) - Scalability considerations - Integration compatibility checks - Monitoring recommendations ### 5. Evaluation Protocol - Step-by-step testing procedure - Sample size recommendations - Scoring methodology - Decision criteria for deployment Include specific test prompts, scoring rubrics, and automated evaluation scripts where possible. Focus on measurable, reproducible results.
Use this when validating custom fine-tuned models before production deployment. The framework helps catch regressions whilst measuring genuine improvements in your target domain. Works with any LLM evaluation pipeline including Claude, GPT-4, and Gemini-based systems.