pull down to refresh
I've never had an end-to-end successful AI automagic experience. It's definitely iterative.
I like to believe that I'm a somewhat skillful operator, and that's partly because of my long years of experience with the work domain, and doing the workflow without AI.
I do wonder the extent to which young people can become skilled operators without having the same years of domain experience.
That being said, I'm not a pessimist on that. People probably wondered if calculators would make people bad at math; and in a way they do, but in another way they also expanded the ability of more people to use math as inputs into bigger projects.
I do wonder the extent to which young people can become skilled operators without having the same years of domain experience.
Be a pal and develop an SOP. I'm slowly developing a huge set myself. I've built reference material from books about how to not just write but evaluate and maintain standard operations and I have a long list of jobs that I have standardized within my own framework that need to be documented and evaluated. Lots of work to do!
I've used AI earlier this year for a huge (in terms of document scope) policy assessment project, but that was not generative: it was only for processing massive amounts of nasty looking scans from typewriter docs made in the 70s and 80s and making it searchable, and then in some cases helping me resolve references to documents published 20-50 years in the past, and what the "changelog" was on it from then to now.
I don't really believe in creating the end product with AI automagically, not in code either - at least not for things that are seeing daylight. I've been amazed at some software that is (or was) high quality, high reputation, and when they start shipping a claude.md in their repo, I start seeing a bunch of crap when I review. For the well maintained stuff, it's mostly only dead code or oddly structured functions. For the poorly maintained stuff... it's costing me a ton of time to make sure that there really isn't an error in "perfect-looking" code, and then I often find what seems to be assumption overload, i.e. poor prompting.
It does get a bit better with the newer models, because they've been trained to do some of the self-adversarial reflection, but I still feel like the one-liner claude code prompts and conversational LLM usage in general are not helping in quality; it all depends on the skills and attention to detail exhibited by the operator.