Fine-tuning vs. RAG for Knowledge Injection: The Decision Engineers Consistently Get Wrong
· 10 min read
A fintech team spent three months fine-tuning a model on their internal compliance documentation — thousands of regulatory PDFs, policy updates, and procedural guides. The results were mediocre. The model still hallucinated specific rule numbers. It forgot recent policy changes. And the one metric that actually mattered (whether advisors trusted its answers enough to stop double-checking) barely moved. Two weeks later, a different team built a RAG pipeline over the same document corpus. Advisors started trusting it within a week.
The fine-tuning team hadn't made a technical mistake. They'd made a definitional one: they were solving a knowledge retrieval problem with a behavior modification tool.
