You Spent $35,000 Fine-Tuning a Model. A $28,000 RAG System Would Have Done It Better. (opens in new tab)

The most expensive mistake in enterprise AI right now is fine-tuning when retrieval is the actual answer. The Decision That Costs More Than It Should When an enterprise AI project needs domain-specific knowledge, two paths appear obvious. Fine-tune the model on your data. Or build a retrieval system that feeds the model your data at query time. Most teams spend weeks debating the question. Then they choose wrong. Over 70% of enterprise AI teams deploying LLMs in production use RAG as their pr...