While llm don't learn, they have now enough token input space and handle context well enough to manage a cyclic conversation. While not learning, they can stil

While llm don't learn, they have now enough token input space and handle context well enough to manage a cyclic conversation. While not learning, they can still run inferences on what they are fed as context within the token space. And because they are becoming so good at getting context out of structured data, they can use the context provided as a layer on top of their knowledge. So you can at a very good approximation upload knowledge into them, and such knowledge can represent how to use tools to fetch data they don't know about - i.e. what langchain agents are doing. Now of course that is not 'llm' some, is llm + code for informaron discovery, but that code is entirely supported and enabled by the existence of good enough llm