How Better Systems Reduce LLM Hallucinations
About This Episode
In this episode of the AI Agents Podcast, host Demetri Panici sits down with David Petrou, founder and CEO of Continua AI, to discuss one of the biggest concerns surrounding large language models: hallucinations.
As AI systems evolve, so does our understanding of what they can (and can’t) do. Instead of treating hallucinations as mysterious failures, this discussion reframes them as a natural outcome of how models generate language—and explains how better systems, safeguards, and context dramatically reduce risk.
The key takeaway? AI doesn’t “hallucinate” in a human sense—it generates probabilities. And as models improve and organizations implement stronger protections, accuracy continues to rise.
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Transcript
And that goes in I guess there's two axes there maybe three. one is u the quality of the models right so back in April 2023 when I started Continua um hallucinations that that was the big issue >> so um you had one camp saying um well this is inherent it's never going to go away um and that sort of puts a ceiling on on the utility of these models another camp uh was well look there's been a lot of progress really fast um a lot of really really really smart people are working on problem. Um, it will go down and and I think it has gone down. Um, >> absolutely. >> Yeah. I mean, and the whole thing about hallucination is it it's like I mean, all models do is is just predict the next token. So, to the extent it gets it right or
gets it wrong, it's doing the same thing. So, hallucination is is probably the wrong mental model um for for how these things work. Um, >> wait, explain what you mean a little bit more by that just so everyone can kind of get more context and then feel free to continue. >> Yeah. Yeah. It's not that like how do I put this? Um, and by the way, continue as sort of a play on on the whole notion of what an LLM does. You know, it just continues what you give it. Um, the whether or not a right answer or wrong answer comes out, it's it's not like we should be amazed just that it gets the syntax of language correct, that it gets the physics of of of how these things work. And then if if it says something wrong or says something right, we just
have to think about well what went into the training data. Um how does this thing work? How does it sample you know the distributions coming out to you know to actually continue the temperature settings all of that. When you understand how these things work it it sort of demystifies. It's like well okay of course it's going to say some right things some wrong things. Now, let's build up um systems and protections and guard rails that get it to do more right than wrong. It's not like u the thing has some like extra capacity to hallucinate in a sort of anthropomorphized way. So, I guess what I'm saying is hallucination is a good um sort of like um shortorthhand linguistic shortorthhand for some behavior that we don't want. But it has no analogy to what a human might be doing.