From the course: Secure Generative AI and LLM Deployment
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Observability for LLMs
From the course: Secure Generative AI and LLM Deployment
Observability for LLMs
Once an internal LLM system is live, the riskiest thing you can do is stop paying attention to it. If you can't see what it's doing, you can't tell whether it's helping people, confusing them, or quietly causing damage. That's what observability is about, making the system visible. For LLMs, there are a few kinds of things you really want to be able to see. First is usage. Who is using the system? How often? From where? At what times? What features or tools are they actually using? This helps you spot weird patterns like sudden spikes, usage strange hours, or accounts that are behaving differently than usual. Second, prompts and responses, but carefully. You don't always want to store raw text, especially if it can contain sensitive data, but you still need some way to understand what's being asked and what's being answered. That might mean hashing, redacting, sampling, or tagging prompts with categories like finance, HR, code, or customer data. This is how you detect misuse, like…
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