AI Observability Monitoring and Debugging LLMs in Production

Gain the practical skills to detect, monitor, and debug LLM failures in production. Review failure modes, monitoring, debugging, and the importance of observability.
Course details

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Most teams don't realize their LLM is misbehaving until a user complains. This course helps you get ahead of that, covering how LLMs (large language models) fail, why the failures are difficult to catch, and what you can do about it. Review the failure modes that show up most in production: hallucinations, prompt injection, drift, and toxic outputs, then move into the practical side of observability-what to measure, what to watch, and how to debug when things go sideways. Get hands-on with Langfuse and Arize Phoenix, two free tools that give you real visibility into what your LLM is actually doing. Leave with a monitoring strategy and debugging approach you can put to work straight away - usable across a variety of roles and backgrounds.
Skills covered

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Software Observability, Large Language Model Operations (LLMOps), Generative AI, Artificial Intelligence (AI)
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