A mid-sized fintech spent nine months and $400K training a custom model on internal documentation. Six weeks after launch, the team quietly switched back to GPT-4 with RAG. The trained model couldn’t keep up with policy changes, hallucinated on edge cases, and required constant retraining. The problem wasn’t the model, but the decision to train in the first place.
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