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Making o1, o3, and Sonnet 3.7 hallucinate for everyone

bengarcia.dev

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Re: Making o1, o3, and Sonnet 3.7 hallucinate for everyone

#91

Post by andix »

Lorak_ wrote: post #87
andix wrote: post #78

Earlier quoted context omitted.

I don't see the point in AI code completions, they are just distracting noise. I'm only doing bigger changes with AI. Prompt based stuff, like "extract the filtering part from all API endpoints in folder abc/xyz. Find a suitable abstraction and put this function into filter-utils.codefile"

What tools do you use to perform such tasks?

Aider. I tried Cursor too, but I don't like VS Code and not being able to chose the LLM provider. I think there are already a lot of tools that perform kind of equally.

Re: Making o1, o3, and Sonnet 3.7 hallucinate for everyone

#92

Post by andrepd »

dominicq wrote: post #5

ChatGPT used to assure me that you can use JS dot notation to access elements in a Python dict. It also invented Redocly CLI flags that don't exist. Claude sometimes invents OpenAPI specification rules. Any time I ask anything remotely niche, LLMs are often bad.

My rule of thumb is: is the answer to your question on the first page of google (a stackoverflow maybe, or some shit like geek4geeks)? If yes GPT can give you an answer, otherwise not.

Re: Making o1, o3, and Sonnet 3.7 hallucinate for everyone

#94

Post by johnisgood »

andix wrote: post #30

I've got a lot of hallucinations like that from LLMs. I really don't get how so many people can get LLMs to code most of their tasks without those issues permanently popping up.

I have made large projects using Claude, with success. I know what I want to do and how to do it, maybe my prompts were right.

Re: Making o1, o3, and Sonnet 3.7 hallucinate for everyone

#96

Post by johnisgood »

bakugo wrote: post #60
andix wrote: post #30

I've got a lot of hallucinations like that from LLMs. I really don't get how so many people can get LLMs to code most of their tasks without those issues permanently popping up.

> I really don't get how so many people can get LLMs to code most of their tasks without those issues permanently popping up They can't, they usually just don't understand the code enough to notice the issues immediately. The perceived quality of LLM answers is inversely proportional to the user's understanding of the topic they're asking about.

Exactly.

Re: Making o1, o3, and Sonnet 3.7 hallucinate for everyone

#97

Post by jeanlucas »

joelthelion wrote: post #84

Hallucinations like this could be a great way to identify missing features or confusing parts of your framework. If the llm invents it, maybe it ought to be like this?

Only if you wanna optimize exclusively for LLM users in this generation.

Re: Making o1, o3, and Sonnet 3.7 hallucinate for everyone

#99

Post by [deleted] »

AlotOfReading wrote: post #45
skissane wrote: post #28

Earlier quoted context omitted.

I think a lot of these issues could be avoided if, instead of just a raw model, you have an AI agent which is able to test its own answers against the actual software… it doesn’t matter as much if the model hallucinates if testing weeds out its hallucinations. Sometimes humans “hallucinate” in a similar way - their memory mixes up different programming languages and they’ll try to use syntax from one in another… but…

Testing is better than nothing, but still highly fallible. Take these winning examples from the underhanded C contest [0], [1], where the issues are completely innocuous mistakes that seem to work perfectly despite completely undermining the nominal purpose of the code. You can't substitute an automated process for thinking deeply and carefully about the code. [0] https://www.underhanded-c.org/#winner [1] https://www…

[deleted]

Re: Making o1, o3, and Sonnet 3.7 hallucinate for everyone

#100

Post by bakugo »

mlyle wrote: post #72
bakugo wrote: post #60

Earlier quoted context omitted.

> I really don't get how so many people can get LLMs to code most of their tasks without those issues permanently popping up They can't, they usually just don't understand the code enough to notice the issues immediately. The perceived quality of LLM answers is inversely proportional to the user's understanding of the topic they're asking about.

Alternatively, we understand it well, and discard bad completions immediately. When I'm using llama.vim, like 40% of what it writes in a 4-5 line completion is exactly what I'd write. 20-30% is stuff that I wouldn't judge coming from someone else, so I usually accept it. And 30-40% is garbage... but I just write a comment or a couple of lines, instead, and then reroll the dice. It's like working through a junior engi…

I've tried using basic AI completions before and found that the signal-to-noise ratio wasn't quite good enough for my taste in my use cases, but I can totally understand it being good enough for others.

My comment was more about just asking questions on how to do things you're totally clueless about, in the form of "how do I implement X using Y?" for example. I've found that, as a general rule, if I can't find the answer to that question myself in a minute or two of googling, LLMs can't answer it either the majority of the time. This would be fine if they said "I don't know how to do that" or "I don't believe that's possible" but no, they will confidently make up code that doesn't work using interfaces that don't exist, which usually ends up wasting my time.

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