Ontology is a so-called white-box approach for AI: all dependencies are clearly stated and formalised. If something does not work as you wish, unlike LLMs, in ontology world you can clearly diagnose where the system fails. I understood the NVIDIA's idea this way: we take unreliable LLM and make a "reliable" ontology using it. If you look back into history, Prolog , was exactly a sort of "memory system" or, better put, a human-friendly database. Ontology is a further development of this concept.

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gb2d_hn 3 hours ago | parent [-]

What can an agent infer from this system that it can't infer from the codebase?

	▲	matthieu_bl 2 hours ago \| parent [-]
		1. What is not in the codebase. Obvious of course, but building an ontology and maintaining it over time forces a company to document what was only living in the head of some experts and never written down. In the case of analytics use cases, think of all the quirks a data analyst might have in mind when querying the data. Very basic e.g. "exclude test accounts whenever you count customers". Every analyst on the team does it without thinking, it's rarely written anywhere, and an agent that doesn't know will happily count them. 2. Related to 1., your codebase or source material may hold different interpretations of the same definition (what "revenue" means can differ between Sales and Finance). The ontology forces you to choose one, or to state when to use each. Without that, the LLM may pick one a bit randomly. 3. It's a governance tool for humans. It gives the company a human-readable, "deterministic" source of truth to agree on what means what, then version it and evolve it.