The One About LLMs in Practice
- Date
- 2025-04-09
- Location
- Take the back entrance and head up to L3!
- Host
- Lorong AI
About this event
Large language models are everywhere right now, but the gap between seeing demos online and actually using them well in real work is still huge. The One About LLMs in Practice is built for people who want to move past the hype, ask better questions, and get clearer on what practical LLM use really looks like in a community setting. This is an in-person gathering for people interested in how LLMs show up in the real world: what they’re good at, where they break, and how people are thinking about applying them responsibly and effectively. If you’ve been looking for a way to talk about AI with others who are curious, thoughtful, and hands-on, this is a strong place to start. About the Event This event is a community talk centered on LLMs in practice. That means the focus is not abstract speculation about the future of AI, but grounded discussion around how these systems are being used, tested, and understood today. Expect a format that makes space for learning and conversation. Whether you’re already experimenting with LLM tools or just trying to make sense of the fast-moving landscape, the session is designed to help you build a more useful mental model of what matters in practice. Because this is part of a community setting, the value is not only in the content itself but also in the shared context. You’ll be in a room with other people actively paying attention to AI, which makes it easier to compare notes, pressure-test ideas, and leave with sharper questions than you arrived with. The title sets the tone well: this is about the practical side. Not just what LLMs can theoretically do, but how people are actually approaching them in work, projects, and day-to-day experimentation. What to Expect You can expect an in-person talk experience with a clear focus on applied understanding. The session will likely be most useful if you come ready to listen closely, think critically, and engage with examples, patterns, or use cases related to LLMs. The event atmosphere should suit people who like substance over noise. Rather than chasing vague AI excitement, the discussion is oriented toward making the topic more concrete and usable for attendees with different levels of familiarity. What that can look like in practice: A focused talk format centered on LLMs and real-world application Community learning with other attendees interested in AI, tools, workflows, and practical questions Opportunities for discussion before or after the session, depending on how the gathering flows A chance to clarify your thinking around where LLMs fit into your work, learning, or experimentation If you’ve mostly encountered LLMs through headlines, social posts, or scattered tool demos, this event offers a more grounded way to engage. If you’re already following the space closely, it gives you a chance to connect what you know with how others are approaching the same questions. Why Attend The biggest reason to attend is simple: practical understanding compounds. A single good talk can save you hours of confusion, help you avoid shallow assumptions, and point you toward better ways of evaluating tools and use cases. LLMs are now part of many conversations across product, engineering, operations, education, research, and creative work. Even if you are not building directly with AI, it is increasingly useful to understand the capabilities and limits of these systems in a more precise way. By showing up in person, you also get something online content rarely provides: context. You can hear how others are framing the same topic, notice what questions come up in the room, and calibrate your own perspective against a live community rather than an algorithmic feed. You should attend if you want to: Get beyond AI buzzwords and into more useful discussion Learn from a community context rather than trying to piece everything together alone Build better judgment about when LLMs are genuinely helpful and when they are not Spend time with people who care about applied AI, not just the trend cycle around it For many attendees, the payoff will be clearer thinking. You may leave with new ideas, stronger questions, or a more practical lens for evaluating what to try next. Practical Details When: Wednesday, April 9 at 3:00 PM GMT+8 Location: In person Venue note: Take the back entrance and head up to L3! Because this is an in-person event, give yourself enough time to arrive, find the correct entrance, and get settled before the session begins. The location note matters, so plan to use the back entrance and then go up to Level 3. This event is especially well-suited to people who value being in the room for the conversation. In-person attendance makes it easier to stay focused, meet others in the community, and participate more naturally before or after the talk. If you’re deciding whether to come, the strongest case is this: if LLMs are relevant to your work, your curiosity, or your future plans, spending an afternoon on practical discussion with an AI-focused community is a smart use of time.
Who should attend
This will be a strong fit if you want a more practical, grounded take on LLMs and prefer learning in a room with other curious people. - You’ve been hearing about **LLMs everywhere** and want a clearer sense of what they actually look like in practice - You work in or around **AI, product, engineering, research, operations, education, or creative workflows** and want better judgment about where LLMs are useful - You’re already experimenting with tools and prompts, and you want to **compare your thinking with others** in a community setting - You’re AI-curious but not interested in empty hype; you want **substance, examples, and practical discussion** - You enjoy attending talks where you can **learn something concrete, then continue the conversation in person** - You’re part of the local tech or AI community and want to stay close to how people are thinking about applied LLM use right now You do not need to be an expert to get value from this. If you care about understanding LLMs more clearly and discussing them with people who are paying attention, you’ll likely feel at home here.