This talk introduces Retrieval-Augmented Generation (RAG), a technique that grounds Large Language Model (LLM) responses in retrieved data to enhance accuracy and relevance. Through practical examples with LangChain4j in Java, the session covers RAG fundamentals, including architecture, implementation, and basic chunking. It then progresses to advanced techniques: sophisticated chunking, query compression, routing, metadata filtering, content aggregation, and re-ranking.

Speaker: Guillaume Laforge

Cloud Run is a fully managed platform that enables you to run your code directly on top of Google's scalable infrastructure. Cloud Run is simple, automated, and designed to make you more productive. Learn how to get started, and dive deep into even the most advanced features to learn how to make the most of Cloud Run.

Speaker: Wietse Venema

The Gemma family of open models keeps evolving, empowering developers to build a wide range of AI applications. In this session, we'll provide an overview of the latest Gemma models, highlighting advancements in performance and capabilities. You'll also learn about the growing Gemma ecosystem, product development tools, community resources, and how to evaluate the best models for your project.

Speaker: Gus Martins

When you change prompts or modify the Retrieval-Augmented Generation (RAG) pipeline in your LLM applications, how do you know it’s making a difference? You don’t—until you measure. But what should you measure, and how? Similarly, how can you ensure your LLM app is resilient against prompt injections and avoids providing harmful responses? More robust guardrails on inputs and outputs are needed beyond basic safety settings.

In this talk, we’ll explore various evaluation frameworks such as Vertex AI Evaluation, DeepEval, and Promptfoo to assess LLM outputs, understand the types of metrics they offer, and how these metrics are useful. We’ll also dive into testing and security frameworks like LLM Guard to ensure your LLM apps are safe and limited to precisely what you need.

Speaker: Mete Atamel

This hands-on workshop gets you building with Gemini in Java or Python. You’ll learn how to send prompts to Gemini, handle streaming and synchronous responses, build a chatbot, process text and image inputs, extract structured data from raw text, use prompt templates, perform sentiment analysis, and implement Retrieval Augmented Generation (RAG). You’ll even learn how to use Gemma, Google’s open model on your local machine with Ollama. You'll leave with the skills to use Gemini in your Java or Python projects immediately with LangChain and LangChain4j.

Important: Bring your laptop. Limited capacity: please express your interest here

Speakers: Guillaume Laforge, Mete Atamel, Valentin Deleplace and Wietse Venema