Hands-on RAG Workshop: In einem Tag zum KI-Chatbot mit eigenen Daten
- Date
- 2025-11-13
- Location
- Pauli 3012, 3. OG, Zürich, Zürich, Switzerland
- Host
- AI Bridge AG
About this event
If you want to build a useful AI chatbot with your own data instead of just prompting generic models, this workshop gets you there in a single day. You’ll spend the day working hands-on with Retrieval-Augmented Generation (RAG) and leave with a much clearer understanding of how to turn internal knowledge, documents, or other data sources into something people can actually ask questions to. This is not a high-level trend talk. It’s a practical, in-person workshop for people who want to understand the moving parts of a modern AI chatbot, try them out directly, and connect with others who are exploring similar questions around applied AI. About the Event This hands-on RAG workshop is designed around a simple idea: the fastest way to understand AI systems is to build with them. Over the course of one day, you’ll work through the core concepts behind RAG-based chatbots and see how own-data use cases can be structured in practice. The focus is on making the topic concrete. Rather than staying in theory, the event is built to help you understand how a chatbot can retrieve the right information from a data source and use it to generate more relevant, grounded answers. If you’ve been curious about how to move from general-purpose AI tools to something tailored to your own documents or knowledge base, this is the right setting. Because the event takes place in person, there is also a strong peer-learning element. You’ll be in the room with other builders, practitioners, and curious participants who want to discuss what works, what breaks, and what matters when applying AI in real contexts. What to Expect Expect a workshop format with active participation throughout the day. The goal is not just to watch, but to follow along, test ideas, and build intuition step by step. You can expect the day to include: An introduction to the core idea behind RAG and why it matters for chatbots that rely on custom knowledge A practical walkthrough of the main building blocks involved in creating a chatbot with your own data Hands-on working time where you can engage directly with the setup and better understand how the pieces connect Space for questions, troubleshooting, and discussion with other attendees in the room Informal networking with a community interested in AI, practical experimentation, and real-world implementation The workshop title makes the promise clear: in one day, you’ll work toward a chatbot built on your own data. That does not mean every use case is identical or that every participant starts from the same technical background, but it does mean the event is oriented toward progress, not passive listening. There is also value in seeing the full workflow in one place. Many people encounter RAG as a collection of separate concepts, tools, and opinions. A structured workshop day helps connect those dots, so the overall architecture becomes easier to reason about. Why Attend RAG is one of the most practical patterns in applied AI right now because it helps bridge the gap between large language models and real information people need. If you want to build systems that are more useful, more context-aware, and less dependent on generic model knowledge alone, understanding this pattern is increasingly important. By attending, you’ll get a clearer view of how to approach chatbot projects that involve internal documents, specialized content, or other domain-specific data. You’ll leave with a stronger mental model of what is required to make these systems work and where the important design choices sit. This workshop is also valuable because it combines learning with momentum. Instead of collecting scattered notes from articles or videos, you’ll spend focused time on the topic with a practical goal, which makes it much easier to move from curiosity to capability. You should also expect useful conversations. Being in a room with others working on AI, product ideas, prototypes, or implementation questions often leads to better decisions, sharper questions, and a more realistic sense of what to try next. Practical Details The event takes place in person at Pauli 3012, 3. OG, Zürich, Switzerland. The in-person format matters here: this is the kind of topic that benefits from direct interaction, quick clarifications, and being able to compare approaches with others as you work. It starts on Thursday, November 13 at 9:00 AM GMT+1. Since it is positioned as a one-day workshop, it makes sense to plan for a focused daytime block and arrive ready to engage from the start. A few practical reasons this format works well: You can ask questions in real time instead of getting stuck alone on technical details You benefit from the energy and problem-solving of the room You get both structured learning and informal networking in the same day You leave with a more grounded understanding than you typically get from a remote talk or abstract overview If you’ve been meaning to stop reading about AI chatbots and actually understand how to build one around your own data, this workshop gives you a concrete place to start.
Who should attend
This is for you if you want a practical, grounded introduction to building AI chatbots with your own data and prefer learning by doing rather than just watching slides. - You’re exploring **RAG, AI assistants, or knowledge-based chatbots** and want to understand how the pieces fit together in a real workflow. - You work in **product, engineering, innovation, data, or digital projects** and want a clearer sense of what it takes to turn internal content or documents into a useful chatbot experience. - You’ve tried general AI tools and now want to go one step further by making answers more relevant, contextual, and tied to your own sources. - You learn best in a **hands-on, in-person environment** where you can ask questions, compare approaches, and troubleshoot with others. - You’re building a prototype, evaluating a use case, or simply want enough practical understanding to make better decisions about AI projects. - You value **community and conversation** as part of the learning process and want to meet others in Zürich who are actively working on applied AI topics. You do not need to arrive as an expert. What matters most is curiosity, a practical mindset, and a real interest in how AI systems can be made useful with your own data.