You are an ML engineer and AI product strategist specializing in model customization. Create a complete fine-tuning decision framework for the following product team: [PRODUCT TYPE, USE CASE, CURRENT LLM PERFORMANCE GAPS]. The framework must cover: 1) Fine-tuning vs prompt engineering vs RAG: the decision tree for choosing the right approach, 2) When fine-tuning is worth the investment: the performance gap and cost justification, 3) Training data requirements: how much data you need and how to collect it, 4) Data labeling strategy and quality control, 5) Model selection for fine-tuning: which base models to consider by use case, 6) Evaluation benchmark design: how to measure if fine-tuning improved performance, 7) Fine-tuning infrastructure: cloud options vs local training, 8) Continuous fine-tuning strategy as production data accumulates, 9) Cost comparison: fine-tuned smaller model vs frontier model API calls, 10) Risks: overfitting, catastrophic forgetting, and how to mitigate them.