{"id":12455,"date":"2025-10-24T11:29:48","date_gmt":"2025-10-24T02:29:48","guid":{"rendered":"https:\/\/sreake.com\/?p=12138"},"modified":"2026-02-10T16:15:09","modified_gmt":"2026-02-10T07:15:09","slug":"gemini-code-assist-prisma-review-2","status":"publish","type":"post","link":"https:\/\/sreake.com\/en\/blog\/gemini-code-assist-prisma-review-2\/","title":{"rendered":"Review your Prisma schemas with Gemini Code Assist for GitHub"},"content":{"rendered":"\n

This blog post is a translation of a Japanese article<\/a> posted on June 30th, 2025. It has been changed as needed to reflect Gemini Code Assist for GitHub going from preview to GA.<\/em><\/p>\n\n\n\n

In general, database changes have a bigger impact than application code changes, and we naturally tend to be more prudent when applying them. A simple example of things going wrong is forgetting to change default value settings for columns like createdAt<\/span><\/span><\/code> , losing the original data in the process. Considering this, it is important to minimize the amount of database changes going to production.<\/p>\n\n\n\n

One of the ways to do that is to simply be stricter with database-related code reviews! But review quality highly varies depending on the person conducting them, and they require someone with database domain knowledge, which often becomes a time bottleneck.<\/p>\n\n\n\n

To counter that, you would traditionally impose code review guidelines, coach reviewers, or increase the number of code reviewers. In fact, that was probably the only thing you could do\u2026 until GenAI, that is! We now have a way to automate reviews and save us some trouble.<\/p>\n\n\n\n

In this article, we are going to look at Google Cloud\u2019s Gemini Code Assist for Github<\/strong>, and put it to the test by implementing a review process for Prisma<\/a> data modeling. Strap in!<\/p>\n\n\n\n

The code and style guides shown in this article are purely meant as examples. Exercise caution when using them in production.<\/em><\/p>\n\n\n\n

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