I like to think of my interactions with an LLM like I'm explaining a request to a junior engineer or non engineering person. You have to be more verbose to someone who has zero context in order for them to execute a task correctly. The LLM only has the context you provided so they fail hard like a junior engineer would at a complicated task with no experience.

I like to think of my interactions with an LLM like I'm explaining a request to a junior engineer or non engineering person. You have to be more verbose to someone who has zero context in order for them to execute a task correctly. The LLM only has the context you provided so they fail hard like a junior engineer would at a complicated task with no experience.

They are not humans - so yeah I can totally see having to "go to school" to learn how to interact with them.

It's a natural language processor, yes. It's not AGI. It has numerous limitations that have to be recognized and worked around to make use of it. Doesn't mean that it's not useful, though.

You have the right perspective. All of these people hand waving away the core issue here don't realize their own biases. Some of the best these things tout as much as 97% accuracy on tasks but if a person was completely randomly wrong at 3% of what they say you'd call an ambulance and no doctor would be able to diagnose their condition (the kinds of errors that people make with brain injuries are a major diagnostic tool and the kinds of errors are known for major types of common injuries ... Conversely there is no way to tell within an LLM system if any specific token is actually correct or not and its incorrectness is not even categorizable.)