It's a "problem" of compute, I think. If you query without an account on ChatGPT you will see the model look up less stuff and research less, than when you have a paid account and choose "medium" or "high" in the effort slider.

Which makes sense, because of you have looked into search and crawlers you notice that search is actual quite expensive (which is why e.g. Kagi charges a few bucks for search every month).

Gets me with AWS stuff on claude all the time, fortunately there's a official amazon MCP for their docs which helps a lot, but I still have to occasionally tell it to check the docs/mcp.

Came here to say the same thing. Models used to rely heavily on world knowledge from their training data. They are now much better at tool use and deciding when to research a topic, rather than just answering from memory.

I wonder how much that extends to using LLMs for programming. I assume most knowledge of programming language syntax still comes from training data.

ChatGPT once told me I was the target of a sophisticated nation state misinformation campaign when I linked it a Reuters article

>the model said that’s pure fiction.

Were you expecting your model to be updated on current events? Why?

Also the specific event you are referring to is a statistically very improbable event, prior to its actually happening.

>It only acquiesced when I specifically directed it to check Reuters.

Do all models do this? They check in with Reuters? Why would a model think that you asking about an extremely improbable event warranted reaching out to Reuters?

in my chat with gemini it could not differentiate between current events and fiction.

if you point it to the web it got the point, but started treating everything like fiction. so it simply started making up possible scenarios and playing them off as real answers when asked for factual information.

i could not tell what the issue was or how to fix it because the reasoning is encrypted. the obfuscation model spat out something like: 'the user is asking for details about a fictional scenario in which the usa has assassinated the leader of iran'

i really don't like the way big ai companies are going. encrypted thinking, guardrails, adversarial personality, moralizing. it is creating something anti-human.

It can be quite hard to determine what needs a tool call or not. LLMs are not well calibrated to what they know and don't know, and tool calls can add latency and extra costs. There are lots of things that are "obvious" right until they aren't - especially political events and disasters.

all the reasoning still comes from pretraining data

Claude sometimes (and Gemini always) will be lazy and fail to fetch new data from the web. Gemini will also repeatedly lie about it and say that it did when it actually didn’t.

The inference time results are quite different. These models haven’t just been trained with tons of docs, blog entries and videos about Java 8. They’ve also been trained on tons of code examples from simple to complex real world ones.

You’re not going to get all that just by pointing the LLM to the recently released Java 27 documentation. That information is also potentially adding tons of content to your context, which is already filled with tons of other data (your code, other recently released libraries it has to get documentation for, etc).

My experience with Claude is that it doesn’t handle its own training cutoff properly. It responds as though its cutoff is today. Gemini is even worse about this.

I’m going to add this site to my agents files so that they’re explicitly aware of their own limitations.

few more generations they will invent "patch in place"

(with "AI" developing the technique of course)

This is likely being solved with stuff like watermarking which Anthropic just added to Claude... I'd imagine they are testing new data and verifying that training data (or at least the data which do NOT want to be AI generated, i.e actual human text) is not generated, at least with claude.

This is one of the problems that eventually solves itself, somehow

This is one of the jagged mismatches between users and LLM developers. The median user doesn't care or want to know about static models and knowledge cutoffs and whether a model can do tool calls or if tool calls even happened. They just want something that works.

Fortunately increased capabilities seem to make this a basic expectation with new releases.

ollama is just an inference engine - it just runs models.

it must ship with some default old model if you didn't need to explicitly download one

ChatGPT recently started web searching for for basically every general knowledge question, which I found quite odd. Maybe an overcorrection to the issue you were having?

I forgot which was it, ChatGPT or Gemini, but one of them insisted on calling Trump "former president" even when discussing decisions he just announced as president. Lol