Learn Generative AI: Evaluation and Monitoring for LLM Systems
- Date
- 2026-05-14
- Host
- Open Source for AI
About this event
Generative AI demos are easy. Building LLM systems that actually work in the real world is where things get interesting. If you want to understand how to evaluate model behavior, catch failures early, and monitor AI systems after deployment, this event is designed to give you a practical foundation. About the Event This in-person session, Learn Generative AI: Evaluation and Monitoring for LLM Systems, focuses on one of the most important parts of modern AI development: knowing whether your system is doing what you think it is doing. It is built for people who want to move beyond surface-level excitement and get into the mechanics of reliability, quality, and operational awareness in LLM-based products. Rather than treating evaluation and monitoring as afterthoughts, this event puts them at the center of the conversation. As LLM systems become more autonomous and more integrated into user-facing workflows, teams need better ways to assess outputs, measure performance over time, and understand where systems break down. You can expect a community-oriented learning environment grounded in practical questions. What should you evaluate? How do you think about quality when outputs are open-ended? What signals matter once a system is live? This event is a chance to explore those questions with others who care about building stronger AI systems. What to Expect The session will center on the key ideas behind evaluation and monitoring for LLM systems, with a focus on how these ideas apply in practice. Expect discussion around the kinds of metrics, test cases, review processes, and monitoring approaches that help teams gain confidence in generative AI behavior. Topics may include: How to think about evaluating open-ended model outputs The difference between one-time testing and ongoing system monitoring Common failure modes in LLM applications Ways to assess quality, consistency, and usefulness How monitoring supports iteration, trust, and operational decision-making Because this is an in-person event, there is also real value in the room itself. You will be able to hear how others are approaching similar challenges, compare perspectives across different levels of technical experience, and sharpen your own thinking through live discussion. Whether you are early in your AI journey or already building with LLMs, the format is meant to help you leave with clearer mental models. You should expect a session that balances conceptual understanding with applied relevance, rather than getting lost in abstract hype. Why Attend A lot of people are learning how to prompt models. Far fewer are learning how to evaluate whether those systems are dependable, safe enough for the use case, or improving over time. That gap matters. Evaluation and monitoring are what turn experimentation into something more disciplined and useful. Attending this event can help you build a stronger framework for thinking about LLM systems as products and systems, not just as demos. If you are working on AI features, exploring autonomous workflows, or trying to understand the operational side of generative AI, these are concepts that will keep showing up. You will come away with a better sense of: Why evaluation is not a single score or one-time benchmark What monitoring means in the context of LLM applications How to identify weak spots before they become bigger problems How to ask better questions about system quality and performance How other people in the AI community are thinking about these challenges There is also a broader benefit: this event helps you develop a more mature perspective on AI development. In a space full of speed and experimentation, evaluation and monitoring create the feedback loops that make progress real. Practical Details This event takes place in person on Wednesday, May 13 at 6:00 PM PDT. If you are looking for a session where you can learn directly alongside others, ask questions in a shared environment, and engage beyond a screen, the in-person format is a strong reason to attend. The topic is especially relevant for people working across AI, generative AI, LLMs, autonomy, and community-driven learning. You do not need to arrive with every answer. What matters most is curiosity about how LLM systems are evaluated, monitored, and improved. To get the most out of the event, it helps to come ready to think critically about real-world AI behavior. Consider the systems you use, build, or want to build, and where quality can drift, outputs can fail, or trust can break down. That mindset will make the conversation more concrete and more useful. If you care about responsible iteration, stronger AI product thinking, and learning with others who take the details seriously, this event will be worth your time.
Who should attend
This is for people who want a more practical, grounded understanding of how LLM systems are assessed and maintained once the initial demo phase is over. - You are building or exploring **LLM-powered products** and want to understand how to measure output quality, spot issues, and improve system behavior over time. - You work in **AI, machine learning, product, engineering, or technical strategy** and need a clearer framework for evaluating generative AI beyond intuition. - You are curious about **autonomous or semi-autonomous AI systems** and want to learn how monitoring supports trust, reliability, and iteration. - You have experimented with prompts or model integrations and are now asking the harder question: **how do I know this system is working well enough for real use?** - You value **in-person learning and community discussion** and want to compare approaches, hear how others think about failure modes, and ask sharper questions. - You are learning your way into generative AI and want exposure to an area that is increasingly essential for anyone serious about deploying or managing LLM systems. If you want less hype and more substance around what makes AI systems dependable, you will likely feel at home here.