Pretraining vs. Fine-Tuning vs. RAG: Choosing the Right AI Approach

This post details the strategic decision-making process for choosing between AI model pretraining, fine-tuning, and RAG. It outlines the cost, time, control, and performance tradeoffs for each approach, providing real-world scenarios for financial services (pretraining), biotech research (fine-tuning), and enterprise IT (RAG). It also introduces a hybrid approach that combines these techniques, leveraging advancements like LoRA/QLoRA for fine-tuning and multi-hop retrieval/query decomposition for RAG. The post implicitly highlights the need for robust infrastructure to support these diverse AI development strategies, including GPU compute, networking, storage, and orchestration, which aligns with the capabilities of CoreWeave's AI infrastructure.