From the course: Handling Sensitive Data with Cloud and Local AI
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Visualizing LLM risks: Create an interactive UI
From the course: Handling Sensitive Data with Cloud and Local AI
Visualizing LLM risks: Create an interactive UI
Renowned developer Simon Wilson, who shows us this lethal trifecta of LLM risks, also has a blog post that shows us how we can design systems that help prevent prompt injections. Now, I encourage you to read this blog post diligently. I want to show you something cool that we can do to kind of visualize And I like to do this for this pattern. eye that illustrates the following pattern. And I really like creating diagrams and interactive little programs in order to visualize some of these design patterns. So here we have the user asks for a summary of a malicious website. We have that the Privileged Agent coordinates the request. It never sees the raw website content. It stores it in a variable. The Quarantined LLM reads the problematic content and produces a summary as a variable. So basically, we are guarding this Privileged LLM from the malicious content itself. Then the Privileged LLM only sees VAR1. look at this in code soon and instructs the UI to show it. And finally, we have a…
Contents
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Privacy controls in popular AI assistants3m 9s
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(Locked)
Understanding AI and data safety1m 4s
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Build a safety framework for responsible AI use2m 23s
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Choosing an inference platform2m 7s
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Visualizing LLM risks: Create an interactive UI2m 21s
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Build it: Implementing the dual LLM pattern4m 34s
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