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Comment by bob1029 | Hacker News Reader

Fine tuning always felt like something that only works at incredible scale with a lot of data.

Prompt engineering, RAG, etc. provide so much more uplift per unit time invested. Maybe fine tuning makes sense if you have a 9 figure budget and thousands of humans to throw at it. I don't feel this works in a typical startup ecosystem. I couldn't move the needle with the amount of data I had on hand.

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To this point, guaranteeing that your fine-tuning doesn't actually weaken the hallucination-resistance of state-of-the-art base models, by overfitting to specific examples in your training set, is a really hard thing to measure. Unless you have robust ways of validating LLM output so that you can do real cross-validation, few-shot prompts, where you can instruct the LLM exactly how much you want it to generalize from those examples, may be far better at achieving many goals.

What about techniques like LoRA?

I thought these models were already leveraging this technique (or something approximating it) as part of their fine tuning offering.

perhaps my ignorance and my beginner level on the topic are biasing me, but in my experience, fine-tuning the GPT-3.5 turbo has elevated our operation to another level. When we put it into production, it became much faster, more accurate, and cheaper when compared to original gpt4. Our use case is to make unstructured data return a JSON with some key-value entities where the order matters.

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