7 Technical Takeaways from Using Gemini to Generate Code Samples at Scale

Using Generative AI to write code is a well-known task, but relying on it to produce production-ready educational content is a different challenge. When we started using Gemini to assist with our work to expand the breadth of resources available to explain Google Cloud products, we realized we needed something more than just existing , general purpose GenAI-powered apps and tools; we needed a specialized system tailored to our use case. The problem we were solving Google Cloud has over a hundred products. Each product has its own unique set of resources that you can create, manipulate, and query using client libraries for languages like JavaScript, Python, Go, Java, and more. Our team at Google works on the sample code that teaches developers how to use those client libraries. As an example, we have a sample that demonstrates how to list Google Cloud Storage “folder” resources using the @google-cloud/storage-control Node.js client library: code_block <ListValue: [StructValue([('code',