[CuriouslyC]

Prompt engineer won't go away, it'll get more "engineer"-like. Knowing how to describe a point in a model's latent space for the generation you want is here to stay, but the black magic and art aspect of it will go away.

For example, in stable diffusion land, lots of people have intuition about the relationship between certain prompts and the output they produce. That intuition is embedding and training data specific, so it's not really transferrable (even to different fine tuned models for stable diffusion 1.5). However, I use clip interrogation to map the portions of the latent that my prompt is pointing to, evaluate the embedding text to find desirable/undesirable elements, then adjust the prompt or add negative prompts to navigate my generations towards what I want.

[aiappreciator]

Prompt engineering is merely the entry-drug to AI-wrangling.

SD has gotten to the point that someone can fine tune a model (LORAs) with 2 days of time and $2 of GPU time.

There'll be roles for AI wranglers in every large company, where you'll be gathering the dataset and building LORA plugins for the AI to adapt specifically for your codebase/customerbase/documentation etc.

There's also processes involved in building APIs for the AI (AIPI?) to use and interface with your documentation and systems, setting up vector databases, monitoring AI output etc.

People who think there won't be job for expert AI users are just coping. Thinking "haha AI will kill your job too". The steam engine was more powerful than 100 men. In the end it required like 30 people up and down the value chain to support the engines, from coal mining, to coal shoving, to maintenance, to manufacturing.

[majormajor]

I'm not sure most codebases are unique enough for that. There will certainly be some of that at places that are doing new things, but for the average online service backend or frontend app programming tasks, I think things like Copilot will see enough and get trained well enough out of the box to be pretty one-size-fits-all.

There will be a lot of business pressure towards using the "good enough" out of the box ones too. If you've got a team of less than a hundred people, rolling your own "datasets, LORA plugins, APIs for AI, vector databases, monitoring, etc" is a multi-person team and significant chunk of new expense. So is the incremental gain their for small to medium teams with relatively "standard" problems?

Kinda like self-hosting at that scale vs using a cloud vendor.

[whatshisface]

"Language model, write me a python script to finetune a language model."