Tailoring foundation models for your business needs: A comprehensive guide to RAG, fine-tuning, and hybrid approaches

Amazon Web Services presents three methods for customizing foundation models: Retrieval Augmented Generation (RAG), fine-tuning, and a hybrid approach. These techniques allow organizations to adapt AI models to specific business needs, balancing performance and cost-efficiency.

The post compares these approaches using synthetic datasets and AWS services, evaluating metrics like BERTScore, LLM evaluator scores, inference latency, and cost. Results indicate RAG outperformed fine-tuning in accuracy and cost, while fine-tuning had the lowest latency. The hybrid approach achieved similar results to RAG but at a higher cost.