Our assistant quotes last quarter’s pricing. Would you...

The premise of fine-tuning a model on updated pricing should be rejected because pricing data is highly dynamic and changes faster than any reasonable retraining cycle. Fine-tuning a 27B parameter model is computationally expensive, requiring approximately 1,324 GPU-hours per refresh, and the resulting model becomes obsolete as soon as the next price change occurs. In contrast, using that same budget for retrieval-augmented generation (RAG) allows for approximately 15 million queries, providing a much more scalable and accurate solution for frequently updated information.