<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Use Case 4: Toxicity Filtering - How to Deply and Use FortiAIGate</title><link>https://fortinetcloudcse.github.io/faig-training-workshop/04_demo_fortiaigate/04_use_case_4.html</link><description>Toxicity Filtering Toxicity Filtering addresses the risk that an LLM generates harmful, offensive, or dangerous content — whether due to model limitations, jailbreak attacks, or deliberate user manipulation. FortiAIGate classifies content across multiple toxicity dimensions in both directions, blocking harmful prompts before they reach the LLM and harmful responses before they reach the user.&#10;LLM Guardrails Almost all LLMs have built in guardrails that prevent the LLM from responding in a way that would be deemed offensive or harmful. The LLM we are using in this lab has those guardrails. So while it won’t respond to us in a toxic manner, we can still talk to it and FortiAIGate will catch those prompts.</description><generator>Hugo</generator><language>en-US</language><atom:link href="https://fortinetcloudcse.github.io/faig-training-workshop/04_demo_fortiaigate/04_use_case_4/index.xml" rel="self" type="application/rss+xml"/></channel></rss>