In “Engineering Trust: Mitigating AI Hallucinations in Deep Network Troubleshooting,” we explore the pivotal role of AI in network diagnostics. This second installment in our series addresses the fundamental question: Can we trust AI-driven agents for troubleshooting? As AI systems evolve, reliability and trustworthiness are paramount. Key challenges include LLM knowledge gaps, hallucinations, and the use of poor quality data. To ensure dependable operations, we advocate for fine-tuning LLMs for network-specific tasks and employing knowledge graphs to establish a shared context among agents. Our methodology features explicit reasoning, grounded responses, and a local knowledge base, facilitating accurate output. Recognizing that mistakes can occur, we utilize semantic resiliency to enhance system reliability through multiple agent collaboration. The human-in-the-loop approach empowers engineers to maintain control and foster trust. By emphasizing these architectural pillars, Deep Network Troubleshooting aims to provide robust, trustworthy AI solutions for network diagnostics. Join us in shaping the future of autonomous network operations.
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