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Comment by p1esk | Hacker News Reader

Why can’t a model explore its environment (given access) and find and use those tools? Why can’t a model fire up a query (sql, http, or whatever) to do the retrieval if it determines it needs more information?

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I'm talking about Large Language Models - the architectures behind most of the current generative text AI boom.

In order to use tools they need to be run as part of a system that grants them access to tools, eg via the reAct pattern. https://til.simonwillison.net/llms/python-react-pattern

Are you talking about a simple script which executes model’s instructions? It can literally be as simple as connecting the model to my computer command line. I will tell it what I want done and give it my cc number or website creds.

Obviously I’m talking about next gen models, like gpt5/6.

For that a model needs to recursively parse its output. That is not possible with the current architectures, we need Agent Frameworks for that.

I think it's just a matter of semantics - "model" usually refers to a neural network or some similarly pure, deterministic computation, or so I thought.

Though the terminology is incorrect (a model by itself cannot 'perform' word other than encode/decode, the llm agents are developed to do what you're stating. But when you think about an llm agent doing all sorts of things like sql etc, it effectively becomes a RAG (which in itself is a process than a technology)

Does the LLM get trained as the database get builded?

As you train a model it should build it's knowledge database at the same time. I guess that's how it work?

When an LLM get trained on next word guessing, can it search the web to improve his results?

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