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 also think they might never become reliable.

There is a bar below which they are reliable.

"Write a Python script that adds three numbers together".

Is that bar going up? I think it probably is, although not as fast/far as some believe. I also think that "unreliable" can still be "useful".

But what does that mean? If you tell the LLM "Say just 'hi' without any extra words or explanations", do you not get "hi" back from it?

That's literally the wrong way to use LLMs though.

LLMs think in tokens, the less they emit the dumber they are, so asking them to be concise, or to give the answer before explanation, is extremely counterproductive.

I was trying to make a point regarding "reliability", not a point about how to prompt or how to use them for work.

This is relevant. Your example may be simple enough, but for anything more complex, letting the model have its space to think/compute is critical to reliability - if you starve it for compute, you'll get more errors/hallucinations.

Yeah I mean I agree with you, but I'm still not sure how it's relevant. I'd also urge people to have unit tests they treat as production code, and proper system prompts, and X and Y, but it's really beyond the original point of "LLMs aren't reliable" which is the context in this sub-tree.

Sometimes I get "Hi!", sometimes "Hey!".

Which model? Just tried a bunch of ChatGPT, OpenAI's API, Claude, Anthropic's API and DeepSeek's API with both chat and reasonee, every single one replied with a single "hi".

o3-mini-2025-01-31 with high reasoning effort replied with "Hi" after 448 reasoning tokens.

gpt-4.5-preview-2025-02-27 replied with "Hi!"

> o3-mini-2025-01-31 with high reasoning effort replied with "Hi" after 448 reasoning tokens.

I got "hi", as expected. What is the full system prompt + user message you're using?

https://i.imgur.com/Y923KXB.png

> gpt-4.5-preview-2025-02-27

Same "hi": https://i.imgur.com/VxiIrIy.png

Ah right, my bad. Somehow I thought the prompt was only:

    Say just 'hi'

while the "without any extra words or explanations" part was for the readers of your comment. Perhaps kubb also made a similar mistake.

I used empty system prompt.

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"