Perhaps Perl has given me Stockholm Syndrome, but when I look at your escaped regex example, it's extremely natural for me. In fact, I'd say it's a little too simple, because the LLM forgot to exclude unnecessary whitespace:
(REGEXP_MATCHES(commentary,
'!\[\s*([^\]]*?)\s*\]\(\s*([^)]*?)\s*\)', 'g'))[2] AS src,
(REGEXP_MATCHES(commentary,
'!\[\s*([^\]]*?)\s*\]\(\s*([^)]*?)\s*\)', 'g'))[1] AS alt_text
That is just nitpicking a one-off example though, I understand your wider point.
I appreciate the LLM is useful for problems outside one's usual scope of comfort. I'm mainly saying that I think it's a skill where the "time economics" really are in favor of learning it and expanding your scope. As in, it does not take a lot learning time before you're faster than the LLM for 90% of things, and those things occur frequently enough that your "learning time deficit" gets repaid quickly. Certainly not the case for all skills, but I truly believe regex is one of them due to its small scope and ubiquitous application. The LLM can be used for the remaining 10% of really complicated cases.
As you've been using regex for decades, there is already a large subset of problems where you're faster than the LLM. So that problem space exists, it's all about how to tune learning time to right-size it for the frequency the problems are encountered. Regex, I think, is simple enough & frequent enough where that works very well.
> As in, it does not take a lot learning time before you're faster than the LLM for 90% of things, and those things occur frequently enough that your "learning time deficit" gets repaid quickly.
It doesn't matter how fast I get at regex, I still won't be able to type any but the shortest (<5 characters) patterns out quicker than an LLM can. They are typing assistants that can make really good guesses about my vaguely worded intent.
As for learning deficit: I am learning so much more thanks to heavy use of LLMs!
Prior to LLMs the idea of using a 100 line PostgreSQL query with embedded regex to answer a mild curiosity about my use of alt text would have finished at the idea stage: that's not a high value enough problem for me to invest more than a couple of minutes, so I would not have done it at all.
Good points. Also looking at your original example I noticed that not only humans can explain regularities they expect in many different ways (also correcting along the way), they can basically ask LLM to base the result on a reference. In your example you provided a template with an img tag and brackets having different attributes patterns. But one can also just ask for a html-style tag. As I did with the "Please create a regex for extracting image file names when in a text a html-style tag img is met" (not posting it here, but "src" is clearly visible in the answer). So the "knowledge" from other domains is applied to the regex creation.
I appreciate the LLM is useful for problems outside one's usual scope of comfort. I'm mainly saying that I think it's a skill where the "time economics" really are in favor of learning it and expanding your scope. As in, it does not take a lot learning time before you're faster than the LLM for 90% of things, and those things occur frequently enough that your "learning time deficit" gets repaid quickly. Certainly not the case for all skills, but I truly believe regex is one of them due to its small scope and ubiquitous application. The LLM can be used for the remaining 10% of really complicated cases.
As you've been using regex for decades, there is already a large subset of problems where you're faster than the LLM. So that problem space exists, it's all about how to tune learning time to right-size it for the frequency the problems are encountered. Regex, I think, is simple enough & frequent enough where that works very well.