The technology is frustrating because (a) you never know what may require fixing, and (b) you never know if it is fixable by further instructions, and if so, by which ones. You also mostly* cannot teach it any fixes (as an end user). Using it is just exhausting.
*) that is, except sometimes by making adjustments to the system prompt
I think this particular example, of counting letters, is obviously going to be hard when you know how tokenization works. It's totally possible to develop an intuition for other times things will work or won't work, but like all ML powered tools, you can't hope for 100% accuracy. The best you can do is have good metrics and track performance on test sets.
I actually think the craziest part of LLMs is that how, as a developer or SME, just how much you can fix with plain english prompting once you have that intuition. Of course some things aren't fixable that way, but the mere fact that many cases are fixable simply by explaining the task to the model better in plain english is a wildly different paradigm! Jury is still out but I think it's worth being excited about, I think that's very powerful since there are a lot more people with good language skills than there are python programmers or ML experts.
*) that is, except sometimes by making adjustments to the system prompt