Let me preface by saying I'm not skeptical about your answer or think you're full of crap. Can you give me an example or two about a single task that you fine-tune for? Just trying to familiarize myself with more AI engineering tasks.
So my use case currently is admittedly very specific. My company uses LLMs to automate hardware design, which is a skill that most LLMs are very poor at due to the dearth of training data.
For tasks which involve generation of code or other non-natural language output, we’ve found that fine-tuning with the right dataset can lift performance rapidly and decisively.
An example task is taking in potentially syntactically incorrect HDL (Hardware Description Language) code and fixing the syntax issues. Fine-tuning boosted corrective performance significantly.