Generative AI enables you to build amazing new apps, but you may also have concerns. Will it be too complex or too expensive, or will it create security risks? Fortunately, building good gen AI apps has become much easier in the past year, in large part due to updated capabilities within databases. We’ll learn how Google Cloud databases handle key AI technologies like vector search, retrieval-augmented generation (RAG), and orchestration, helping you bridge the gap between general-purpose AI models and your business-specific data. We’ll dive into some fun application examples to see how they were built and how you can use the same techniques in your apps.

As a small or midsize business, adopting generative AI may come with challenges around security, cost, and support resources. Google Cloud databases such as AlloyDB and Cloud SQL can help bridge the gap between gen AI models and the data sitting in your databases. We’ll review Google Cloud database options and learn about their reliability, scalability, and openness benefits. Then we'll dive into some gen AI application examples to see how they were built and how they use data from your database to provide accurate responses.

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