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Code samples to get started building generative AI apps on Google Cloud

February 28, 2024
Donna Schut

Head of Technical Solutions Management, Applied AI Engineering

Kevin Tsai

Head of Solutions Architecture, Applied AI Engineering

Google Cloud Next '24

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Last year, we surveyed over 100 senior executives from leading companies on the top generative AI use cases for 2024. They told us their top use cases delivered near-term benefits for their business and had a lower time to and risk to implement.

Specifically, they identified improving customer service, increasing the productivity of software development and content development, and driving process efficiencies through automation as their most highly-prioritized use cases. For these categories, our team created code samples and best practices to help organizations quickly get started on Google Cloud.

Customer service modernization

Customer service modernization entails boosting customer service representatives’ productivity, improving customer analytics, and increasing deflection rates. Generative AI can support customer service and field agents by enabling them to quickly synthesize answers from internal knowledge bases and external references, as well as summarize historical conversations to more quickly resolve issues. Analysts can easily view call center and customer analytics with predictive models, which enables them to personalize a customer’s experience with gen AI. To improve deflection rates, customer service organizations can use gen AI to auto generate responses for frequently asked questions and enhance their websites with multimodal search and content summarization. Check out the Github repository to get started here.

Website modernization

Gen AI use cases for website modernization can include enhancing content generation and website navigation, which can improve customer engagement and potentially decrease call center volume via improved digital experiences. Organizations can modernize their website by implementing search capabilities and conversational agents that help users easily find information, recommender systems for personalization based on website events, generating images and text for the website using generative AI, and expanding reach through website localization and translation. Check out the Github repository to get started here.

Product cataloging

When new products arrive, retailers would like to get them on their product catalog as quickly as possible. This laborious process includes creating descriptions, identifying attributes, and properly placing the product in the correct product hierarchy. It is not uncommon for these tasks to take several weeks, but with gen AI, the process can be shortened to hours or days, thereby accelerating time to revenue. Check out the Github repository to get started here.

Developer efficiency

Generative AI can help development teams with their inner loop, outer loop, and documentation. This entails code generation that follows organizational guidelines, spots security and performance issues, and creates unit tests. The ability to automate tasks such as vulnerability auto-scanning, streamlining Git processes, and automating release planning efforts can reduce the number of errors. Last but not least, gen AI can help developers search for bugs, code and documentation. Check out the Github repository to get started here.

Internal efficiencies

There are six main areas where we see companies looking to apply generative AI for internal inefficiencies: accounts payable and cash flow management; human resources helpdesk; procurement contract management; travel bookings; compliance processes; and sales, service, and marketing. For each of these areas, organizations can build conversational agents or other generative AI-enabled applications to help their employees easily discover and process information from common enterprise applications and systems, saving employees time that they can spend on more valuable and interesting work. Check out an example of Github repository to get started here.

Get started with building gen AI applications on Google Cloud

Google Cloud offers Vertex AI, an end-to-end ML platform, with state of the art models, enabling you to develop and deploy production-ready applications on scalable AI infrastructure with enterprise-grade security. Vertex AI provides data scientists with innovative models including but not limited to: our new family of Gemini models that include multimodal capabilities, PaLM 2 for text, Imagen for image generation and editing, Codey for code generation, and over 100 open-source and third-party models. Using Vertex AI Search, Developers can quickly build multimodal search with grounding and advanced features like top results summarization, extractive answers, and snippets. With Vertex AI Conversation, customers can build engaging conversational agents that facilitate multi-turn conversations and grounded responses. Moreover, Apigee enables developers to seamlessly integrate with business applications such as Salesforce or Workday.

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