Thread

1. linux_devil · 1785992531 · focus · HN ↗

   Why do we need to train the model to solve for retrieval within the org, so we have to keep training it whenever new dataset is introduced , or am I missing something here ?

   1. jmalicki · 1785993265 · focus · HN ↗

      It's a matter of cost. Did you see the 100x cheaper?

      If you have a workload that is going to be very heavy, incurring a large training cost to make a cheaper model work well with the dataset will be dramatic cost reduction. Most large AI workloads can't afford, or truly need, the expense or capability of GPT 5.6 Sol when cheaper models can do.

      Of course you could skip that and just use GPT-5.6 Sol everywhere instead. If you're running a fast food restaurant you could hire Michelin star chefs to make your burger and fries without further training. Or you could have a training program for teenagers, a sourcing program, etc. to scale up to your chain to still get consistent quality without needing that level of cost in each store, but replacing it with a centralized repeatable process.

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