Sharon Zhou, CEO and co-founder of Lamini, explains how memory tuning optimizes AI model precision and reduces hallucinations for enterprise applications.
“Been able with memory tuning, which is what I've been working on to remove those hallucinations, to remove that and actually get these models from, you know, not necessarily being general for everything. And instead of being pretty good at everything, but perfect at nothing to be actually perfect or near perfect at some things and still pretty good at everything else.”
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More from Sharon Zhou
AssertionSupported
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“And I do think that's the future so we can get something that is incredibly smart, incredibly huge, but with the latency cost and speed of something, something tiny. So no more big model versus small model paradigm. It's potentially one in the same.”
Adding sequential LLM calls or filters to catch errors fails in production
“It's both of those things, and I think people are addressing error today by adding more calls to the model of filtering. Out the requests. And I think I don't think that'll work for serious production use cases.”
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