I'm not disagreeing with the overall post, but from closely observing end users of LLM-backed products for a while now, I think this needs nuance.
The average joe, be it a developer, random business type, a school teacher or your mum, is very bad at telling an llm what it should do.
- In general people are bad at expressing their thoughts and desires clearly. Frontier LLMs are still mostly sycophantic, so in absence of clear instructions they will make up things. People are prone to treating the LLM as a mind reader, without critically assessing if their prompts are self-contained and sufficiently detailed.
- People are pretty bad at estimating what kind of data an LLM understands well. In general data literacy, and basic data manipulation skills, are beneficial when the use case requires operating on data besides natural language prompts. This is not a given across user bases.
- Very few people have a sensible working model of what goes on in an autoregressive black box, so they have no intuition on managing context
User education still has a long way to go, and IMO is a big determining factor in people getting any use at all from the shiny new AI stuff that gets slathered onto every single software product these days