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The prompts start the dream, and based on the LLM’s hazy recollection of its training documents, most of the time the result goes someplace useful. ” It looks like a bug, but it’s just the LLM doing what it always does. An LLM is 100% dreaming and has the hallucination problem. Remember the context.
Companies are also trying to figure out how to leverage LLMs to make their private data more useful and create their next competitive advantage. While LLM accuracy has improved greatly in a relatively short period of time, today LLMs are still too unreliable for most production use cases, especially in high-stakes domains.
For a detailed overview of predictive AI techniques, please refer to Chapter 4 of my book The Art of AIProductManagement. Navigating the LLM triad While predictive models trained from scratch can excel at very specific tasks, they are also rigid and will refuse to perform any other task.
Today, I received none other than Paige Bailey, a trailblazer in AIproductmanagement and a visionary who’s been at the helm of transformative projects at Google DeepMind and GitHub, working on the most advanced LLM projects. I’m thrilled to bring you this new episode.
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