Retrieval Augmented Generation (RAG) is a powerful technique to provide real time, domain-specific context to the LLM to improve accuracy of responses. RAG doesn't require the addition of sensitive data to the model, but still requires application developers to address security and privacy of user and company data. In this session, you will learn about security implications of RAG workloads and how to architect your applications to handle user identity and to control data access.

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This in-depth technical session delves into real-world use cases, and design patterns for building generative AI applications using Google Cloud Databases, Vertex AI, and popular open-source orchestration frameworks such as LangChain. We’ll showcase a sample application that leverages the versatility of Google Cloud Databases to implement dynamic grounding using various RAG techniques, giving you valuable insights on implementing enterprise-grade gen AI solutions.

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Building an assistant capable of answering complex, company-specific questions and executing workflows requires first building a powerful Retrieval Augmented Generation (RAG) system. Founding engineer Eddie Zhou explains how Glean built its RAG system on Google Cloud— combining a domain-adapted search engine with dynamic prompts to harness the full capabilities of Gemini's reasoning engine. By attending this session, your contact information may be shared with the sponsor for relevant follow up for this event only.

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Accessing mission-critical data in a nonintrusive fashion will be critical for enabling operational analytics, and with the evolution of generative AI, enterprises are building RAG-based gen AI applications that require access to operational data. Datastream is a simple, serverless data-streaming platform that organizes the ingesting, processing, and analyzing operational data to support AI/ML and RAG apps. Experts from RocketMoney and Intuit Mailchimp will share how they’re using Datastream to solve for Operational Analytics and beyond.

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As generative AI applications mature, retrieval-augmented generation (RAG) has become popular for improving large language model-based apps. We expect teams to move beyond basic RAG to autonomous agents and generative loops. We'll set up a Weaviate vector database on Google Kubernetes Engine (GKE) and Gemini to showcase generative feedback loops.

After this session, a Google Cloud GKE user should be able to: - Deploy Weaviate open source on GKE - Set up a pipeline to ingest data from the Cloud Storage bucket - Query, RAG, and enhance the responses

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Startups are setting the bar high for innovation and impact with AI implementations. In this lightning round session, you’ll learn from some groundbreaking startups.

Resemble AI will talk about their cutting-edge text to speech engine integrated with Google's Gemma AI and Gretel.ai will show how they empower developers to create high-quality synthetic data. Learn about Writer AI large language models that are top-ranked on Stanford HELM's leaderboard, as well as their unique graph-based retrieval-augmented generation approach. Join us for a showcase in the latest AI innovation.

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In this session, you’ll learn how to deploy a fully-functional Retrieval-Augmented Generation (RAG) application to Google Cloud using open-source tools and models from Ray, HuggingFace, and LangChain. You’ll learn how to augment it with your own data using Ray on Google Kubernetes Engine (GKE) and Cloud SQL’s pgvector extension, deploy any model from HuggingFace to GKE, and rapidly develop your LangChain application on Cloud Run. After the session, you’ll be able to deploy your own RAG application and customize it to your needs.

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In this session, Douwe Kiela, the CEO and Co-Founder of Contextual AI and an Adjunct Professor in Symbolic Systems at Stanford University, will talk about how Contextual AI is building the next generation of language models leveraging Google Cloud. He will dive deeper into why retrieval augmented generation (RAG; which he pioneered at Facebook) is the dominant paradigm for large language model (LLM) deployments and the role he believes RAG will play in the future of gen AI.

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Natural language is an ideal interface for many real time applications such as inventory tracking, patient journey, field sales, and other on-the-go situations. However, these real time applications also require up to date and accurate information, which necessitates a real time RAG architecture. In this session, we will demonstrate how you can build an accurate and up to date real time generative AI application using a combination of Dataflow and graph databases.

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Building an assistant capable of answering complex, company-specific questions and executing workflows requires first building a powerful Retrieval Augmented Generation (RAG) system. Founding engineer Eddie Zhou explains how Glean built its RAG system on Google Cloud— combining a domain-adapted search engine with dynamic prompts to harness the full capabilities of Gemini's reasoning engine. By attending this session, your contact information may be shared with the sponsor for relevant follow up for this event only.

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Unlock the power of retrieval-augmented-generation (RAG) for generative AI apps with Vertex AI app builder platform. Vertex AI Vector Search powers blazingly fast embeddings retrieval for your search, recommendations, ad serving, and other gen AI applications. Multimodal embeddings scale your search across text, images, and other modalities. Customize your RAG pipeline with document understanding capabilities. With comprehensive offerings, Vertex AI makes building robust RAG systems easy.

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Explore the transformative power of Generative AI with Databricks on Google Cloud. Learn to integrate and leverage governed, sensitive data for real-world problem-solving, from advanced RAG applications to traditional challenges. With 88% of enterprises investing in Generative AI, discover how to swiftly develop and deploy tailored solutions, which could include Q&A bots and custom models. Gain insights from a customer’s experience and propel your organization into the forefront of AI innovation. By attending this session, your contact information may be shared with the sponsor for relevant follow up for this event only.

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In this workshop, you will learn how you can easily create a Retrieval Augmented Generation (RAG) application and how to use it. We will be highlighting AlloyDB Omni (our deploy-anywhere version of AlloyDB) with pgvector's vector search capabilities. You will learn to run an LLM and embedding model locally so that you can run this application anywhere. Creating an app in a secure way with LLMs playing around your data is harder than ever. Come and build with me!

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AI is all the rage these days, but how can you make practical use of it without spending months of time learning this new technology? This session explains how to build an AI-powered content search tool for your own content in an afternoon, with a useful AI development pattern called retrieval augmented generation (RAG). We will demonstrate an updated version of the Docs Agent project that uses the Gemini API.

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This presentation explores deploying retrieval augmented generation (RAG) on Vertex AI Search to enhance QAD's internal data search (Jira, Confluence, Google Sites). Discover how GenAI improves query responses, utilizing a user-friendly web app on Google App Engine to counteract the loss of institutional knowledge. Join us for insights into this innovative enterprise search solution. By attending this session, your contact information may be shared with the sponsor for relevant follow up for this event only.

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The advent of Generative AI has ushered in an unprecedented era of innovation, marked by the transformative potential of Large Language Models (LLMs). The immense capabilities of LLMs open up vast possibilities for revolutionizing business ops and customer interactions. However, integrating them into production environments presents unique orchestration challenges. Successful orchestration of LLMs for Retrieval Augmented Generation (RAG) depends on addressing statelessness and providing access to the most relevant, up-to-date information. This session will dive into how to leverage LangChain and Google Cloud Databases to build context-aware applications that harness the power of LLMs. Please note: seating is limited and on a first-come, first served basis; standing areas are available

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RAG (retrieval-augmented generation) systems ground generative AI models in your own data, ensuring factuality, relevance, and control in the performance of your enterprise applications. However, building a RAG system from scratch is complex. In this session we'll share how Vertex AI Search can serve as an out-of-the-box RAG system for enterprise applications, handling data management, embedding, indexing, and retrieval with minimal setup time. Follow along to see how you can build and deploy a RAG-powered app in minutes and hours.

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Large Language Models (LLMs) have changed the way we interact with information. A base LLM is only aware of the information it was trained on. Retrieval augmented generation (RAG) can address this issue by providing context of additional data sources. In this session, we’ll build a RAG-based LLM application that incorporates external data sources to augment an OSS LLM. We’ll show how to scale the workload with distributed kubernetes compute, and showcase a chatbot agent that gives factual answers.

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Discover how to easily integrate Google-quality search in all customer and employee experiences. With Vertex AI, organizations can ground their generative AI applications with enterprise data, providing advanced RAG Search functionality and low total cost of ownership.

Click the blue “Learn more” button above to tap into special offers designed to help you implement what you are learning at Google Cloud Next 25.

Building an assistant capable of answering complex, company-specific questions and executing workflows requires first building a powerful Retrieval Augmented Generation (RAG) system. Founding engineer Eddie Zhou explains how Glean built its RAG system on Google Cloud— combining a domain-adapted search engine with dynamic prompts to harness the full capabilities of Gemini's reasoning engine. By attending this session, your contact information may be shared with the sponsor for relevant follow up for this event only.

Click the blue “Learn more” button above to tap into special offers designed to help you implement what you are learning at Google Cloud Next 25.