[illiac786]

Agree.

I find that, for many, LLMs are addictive, a magnet, because it offers to do your work for you, or so it appears. Resisting this temptation is impossibly hard for children for example, and many adults succumb.

A good way to maintain a healthy dose of skepticism about its output and keep on checking this output, is asking the LLM about something that happened after the training cut off.

For example, I asked if lidar could damage phone lenses. And the LLM very convincingly argued it was highly improbable. Because that recently made the news as a danger for phone lenses, and wasn’t part of the training data.

This helps me stay sane and resist the temptation of just accepting LLM output =)

On a side note, the kagi assistant is nice for kids I feel because it links to its sources.

[dale_glass]

LIDAR damaging the lens is extremely unlikely. A lens is mostly glass.

What it can damage is the sensor, which is actually not at all the same thing as a lens.

When asking questions it's important to ask the right question.

[illiac786]

Sorry, I meant the sensor

[criley2]

I asked ChatGPT o3 if lidar could damage phone sensors and it said yes https://chatgpt.com/share/683ee007-7338-800e-a6a4-cebc293c46...

I also asked Gemini 2.5 pro preview and it said yes. https://g.co/gemini/share/0aeded9b8220

I find it interesting to always test for myself when someone suggests to me that a "LLM" failed at a task.

[illiac786]

I should have been more specific, but you missed my point I believe.

I tested this at the time on Claude 3.7 sonnet, which have an earlier cut off date and I just tested again with this prompt: “Can the lidar of a self driving car damage a phone camera sensor?” and the answer is still wrong in my test.

I believe the issue is the training cut off date, that’s my point, LLM seem smart but they have limits and when asked about something discovered after training cut off date, they will sometimes confidently be wrong.

[criley2]

I didn't miss your point, rather I wanted you to realize some deeper points I was trying to make

- Not all LLM are the same, and not identifying your tool is problematic because "LLM's can't do a thing" is very different than "The particular model I used failed at this thing". I demonstrated that by showing that many LLMs get the answer right. It puts the onus of correctness entirely on the category of technology, and not the tool used or the skill of the tool user.

- Training data cutoffs are only one part of the equation: Tool use by LLM's allows them to search the internet and run arbitrary code (amongst many other things).

In both of my cases, the training data did not include the results either. Both used a tool call to search the internet for data.

Not realizing that modern AI tools are more than an LLM with training data, but rather have tool calling, full internet access, and can access and reason about a wide variety of up to date data sources demonstrates a fundamental misunderstanding about modern AI tools.

Having said that:

Claude Sonnet 4.0 says "yes": https://claude.ai/share/001e16f8-20ea-4941-a181-48311252bca0

Personally, I don't use Claude for this kind of thing because while it's proven to be a very good at being a coding assistant and interacting with my IDE in an "agentic" manner, it's clearly not designed to be a deep research assistant that broadly searches the internet and other data sources to provide accurate and up to date information. (This would mean that ai/model selection is a skill issue and getting good results from AI tools is a skill, which is borne out by the fact that I get the right answer every time I try, and you can't get the right answer once).

[illiac786]

Still not getting it I think.

My point is: LLMs sound very plausible and very confident when they are wrong.

That’s it. And I was just offering a trick to help remembering this, to keep checking their output – nothing else.