As AI adoption accelerates, many enterprises still face challenges building production-grade AI systems for high-value, knowledge-intensive use cases. RAG 2.0 is Contextual AI’s unique approach for solving mission-critical AI use cases, where accuracy requirements are high and there is a low tolerance for error. 

In this talk, Douwe Kiela—CEO of Contextual AI and co-inventor of RAG—will share lessons learned from deploying enterprise AI systems at scale. He will shed light on how RAG 2.0 differs from classic RAG, the common pitfalls and limitations while moving into production, and why AI practitioners would benefit from focusing less on individual model components and more on the systems-level perspective. You will also learn how Google Cloud’s flexible, reliable, and performant AI infrastructure enabled Contextual AI to build and operate their end-to-end platform.

Unlock the power of generative AI with retrieval augmented generation (RAG) on Google Cloud. In this session, we’ll navigate key architectural decisions to deploy and run RAG apps: from model and app hosting to data ingestion and vector store choice. We’ll cover reference architecture options – from an easy-to-deploy approach with Vertex AI RAG Engine, to a fully managed solution on Vertex AI, to a flexible DIY topology with Google Kubernetes Engine and open source tools – and compare trade-offs between operational simplicity and granular control.

This session explores building sensitive data protection directly into Retrieval-Augmented Generation (RAG) architectures. We'll demonstrate how to leverage Cloud Data Loss Prevention (Cloud DLP) and the Faker Library to anonymize sensitive data within the RAG pipeline. The session will cover techniques for reversible transformations using Memorystore and Firestore for data mapping, and discuss integrating these methods with Large Language Models (LLMs) like Gemini via LangChain and Vertex AI Search. Learn how to create secure and compliant AI solutions that protect sensitive data and adhere to regulations like the EU AI Act.

What if building an AI agent that thinks, reasons, and acts autonomously took less time than your coffee break? With Vertex AI, it’s not just possible. It’s easy. Join DoiT to see how to build, deploy, and scale a production-ready AI agent in 10 minutes using Google’s top services: Gemini 2 for language understanding, the RAG Engine for fetching information, and the Agent Engine for orchestration. To top it off, watch a live demo take an agent from concept to production-ready in real time.

This Session is hosted by a Google Cloud Next Sponsor.
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Dive deep into the world of multimodal analytics with BigQuery. This session explores how to unlock insights from all data types in BigQuery using embeddings generation and vector search. We’ll demonstrate how BigQuery object tables combine text, documents, and images to unlock popular use cases like recommendation engines and retrieval-augmentation generation (RAG). Learn how to leverage BigQuery as a knowledge base to ground your cutting-edge AI application with your own enterprise data.

AI applications that use the retrieval augmented generation (RAG) architecture can be a lot more accurate than those built with just the AI model’s built-in knowledge. But RAG introduces additional processing steps which need to be monitored. We’ll show you how.

Concerned about AI hallucinations? While AI can be a valuable resource, it sometimes generates inaccurate, outdated, or overly general responses - a phenomenon known as "hallucination." This hands-on lab teaches you how to implement a Retrieval Augmented Generation (RAG) pipeline to address this issue. RAG improves large language models (LLMs) like Gemini by grounding their output in contextually relevant information from a specific dataset. Learn to generate embeddings, search vector space, and augment answers for more reliable results.

If you register for a Learning Center lab, please ensure that you sign up for a Google Cloud Skills Boost account for both your work domain and personal email address. You will need to authenticate your account as well (be sure to check your spam folder!). This will ensure you can arrive and access your labs quickly onsite. You can follow this link to sign up!

Want to create a retrieval-augmented generation (RAG) system perfectly tailored to your data and use case? This session dives into building custom RAG pipelines on Vertex AI. Learn how to combine Vertex AI services like document processing, vector search, and grounding to build powerful, context-aware AI applications.

Build more capable and reliable AI systems by combining context-aware retrieval-augmented generation (RAG) with agentic decision-making in an enterprise AI platform, all in Java! This session covers everything from architecture, context construction, and model routing to action planning, dynamic retrieval, and recursive reasoning, as well as the implementation of essential guardrails and monitoring systems for safe deployments. Learn about best practices, trade-offs, performance, and advanced techniques like evaluations and model context protocol.

Move your generative AI projects from proof of concept to production. In this interactive session, you’ll learn how to automate key AI lifecycle processes—evaluation, serving, and RAG—to accelerate your real-world impact. Get hands-on advice from innovative startups and gain practical strategies for streamlining workflows and boosting performance.

Build a multimodal search engine with Gemini and Vertex AI. This hands-on lab demonstrates Retrieval Augmented Generation (RAG) to query documents containing text and images. Learn to extract metadata, generate embeddings, and search using text or image queries.

If you register for a Learning Center lab, please ensure that you sign up for a Google Cloud Skills Boost account for both your work domain and personal email address. You will need to authenticate your account as well (be sure to check your spam folder!). This will ensure you can arrive and access your labs quickly onsite. You can follow this link to sign up!

UKG is revolutionizing workforce management with AI agents and retrieval-augmented generation (RAG) systems. Join this session for a deep dive into how UKG, Google Cloud, and MongoDB collaborated to orchestrate enterprise data and put it to use powering intelligent, context-aware AI solutions that shape the future of work.

This Session is hosted by a Google Cloud Next Sponsor.
Visit your registration profile at g.co/cloudnext to opt out of sharing your contact information with the sponsor hosting this session.

Want to control the output of your AI agents? This session explores essential Agent Ops practices, including metrics-driven development, large language model (LLM) evaluation with retrieval-augmented generation (RAG) and function calling, debugging with Cloud Trace, and learning from human feedback. Learn how to optimize agent performance and drive better business outcomes.

Unlock the true potential of your enterprise data with AI agents that transcend chat. This panel explores how leading companies build production-ready AI agents that deliver real-world impact. We’ll examine Google Cloud, MongoDB, Elastic, and open source tools, including generative AI and large language model (LLM) optimization with efficient data handling. Learn practical approaches and build the next wave of AI solutions.

Applications of the future require a database that transcends historical paradigms. They require advanced in-database capabilities like Graph RAG, vector and full-text search without compromising on critical database properties of compliance, scale, and availability. In this talk, you'll learn how Spanner's native search and interoperable multi-model capabilities enable your developers to build intelligent, global applications on a single, zero-ETL (extract, transform, and load) data platform.

Building product recommendation system/chat bot using LLMs is simple... on paper. In reality, simple RAG covers only the simplest scenarios. To cover more complicated one, you may want to learn about such things as a conversation graph, logical and semantic routing, hybrid search etc. In this talk I share lessons and tricks we have learn during building product recommendation system using Gemini.

This hands-on lab empowers you to build a cutting-edge multimodal question answering system using Google's Vertex AI and the powerful Gemini family of models. By constructing this system from the ground up, you'll gain a deep understanding of its inner workings and the advantages of incorporating visual information into Retrieval Augmented Generation (RAG). This hands-on experience equips you with the knowledge to customize and optimize your own multimodal question answering systems, unlocking new possibilities for knowledge discovery and reasoning.

If you register for a Learning Center lab, please ensure that you sign up for a Google Cloud Skills Boost account for both your work domain and personal email address. You will need to authenticate your account as well (be sure to check your spam folder!). This will ensure you can arrive and access your labs quickly onsite. You can follow this link to sign up!

Learn how to leverage Gemini's long context for creating and interpreting data analytics queries using Agentic Retrieval Augmented Generation with NL2SQL techniques and reasoning.

Join this session to learn how to ground your AI with relevant data with retrieval-augmented generation (RAG) from Firebase Data Connect, which brings rapid development and intelligent context from your Cloud SQL database to your generative AI experiences. Data Connect makes it easy to connect your app, data, and AI all together, and seamlessly integrates Vertex AI and Cloud SQL to make RAG easy and ready for AI agents.

This session will cover Claude’s advanced reasoning capabilities, prompt engineering techniques, and empirical evaluations. We’ll also delve into best practices for prompt engineering, and how to optimize agents to advance Retrieval-Augmented Generation (RAG) within your Google Cloud environment. Whether you're an AI practitioner or an enterprise architect, this session will equip you with the knowledge to harness Claude’s full potential for enhanced AI workflows on Google Cloud.

This Session is hosted by a Google Cloud Next Sponsor.
Visit your registration profile at g.co/cloudnext to opt out of sharing your contact information with the sponsor hosting this session.