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Comment by Palmik | Hacker News Reader

Where fine tuning shines imo:

Things that require consistency: e.g. you want the chat / output to have certain "personality", consistent level of conciseness or formatting.

Things where examples are hard to fit into prompt: e.g. summarization, or other longer form tasks.

High volume, simpler tasks: Various data extraction tasks.

Two of my side projects (links in bio) use AI for summarization, and indeed consistency is a big issue there.

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You can also fine-tune gpt-3.5 though.

Fine tuned gpt-3.5 is ~8x the price compared to regular gpt-3.5. It might be a good way to collect better training data though. :)

That's absolute dirt cheap. It's like 1/10th the price fine-tuned GPT-3 was launched at, and funnily enough cheaper than many of the original embeddings endpoints.

It's great that the costs are coming down, but it's still not economical for many activities.

The fact that some things had extreme margins before, and now they have less extreme margins, isn't a really good indicator.

They made a technological advancement that allowed them to provide better output cheaper and faster and passed the savings onto their users...

Interesting to spin that into "not a good indicator".

Yes, and that's great, but what I meant is that it can definitely be much cheaper still, especially if you do not need a model that needs to be able to do everything, but is specialized on one or a few use cases.

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