Comment by danielodievich

How is that comment relevant to this story about OpenAI's response to perceptions that Google has gained in market share?

Relying on the model’s own “memory” to answer factual queries is almost always a mistake. Fine-tuning is almost always a more complex, more expensive and less effective method to give a model access to a knowledge base.

However using the model as a multi-hop search robot, leveraging it’s general background knowledge to guide the research flow and interpret findings, works exceedingly well.

Training with RL to optimize research tool use and reasoning is the way forward, at least until we have proper Stateful LLMs that can effectively manage an internal memory (as in Neural Turing Machines, and such).

"trained on our public and internal docs" trained how? Did you mean fine-tuned haiku? Did you actually fine tune correctly? Its not even a recommended architecture.

Or did you just misuse basic terminology about LLMs and are now saying it misbehaved, likely because your org did something very bad with?

All depends on the tasks and the prompting engineers.

Even with your intelligence you would need years to deliver something like this: https://github.com/7mind/jopa

The outcome will be better for sure, but you won't do anything like that in a couple of weeks. Even if you have a team of 10. Or 50.

And I'm not an LLM proponent. Just being an empirical realist.

I don't know man.

My code runs in 0.11s

Gemini's code runs in 0.5s.

Boss wants an explanation. ¯\_(ツ)_/¯

Yeah, LLMs are not really good about things that can't be done.

At some point you'll be better off with implementing features they hallucinated. Some people with public APIs already took this approach.