Its perfectly reliable for the things you know it to be, such as operations within its context window size.

Don't ask LLMs to "Write me Microsoft Excel".

Instead, ask it to "Write a directory tree view for the Open File dialog box in Excel".

Break your projects down into the smallest chunks you can for the LLMs. The more specific you are, the more reliable it's going to be.

The rest of this year is going to be companies figuring out how to break down large tasks into smaller tasks for LLM consumption.

They're reliable already if you change the way you approach them. These probabilistic token generators probably never will be "reliable" if you expect them to 100% always output exactly what you had in mind, without iterating in user-space (the prompts).

I remember when people were saying here on HN that AIs will never be able to generate picture of hands with just 5 fingers because they just "don't have common sense"

while True:

  print("This model that just came out changes everything. It's flawless. It doesn't have any of the issues the model from 6 months ago had. We are 1 year away from AGI and becoming jobless")
  sleep(timedelta(days=180).total_seconds)

Usable LLMs are 3 years old at this point. ChatGPT, not Github Copilot, is the marker.