Beyond sqlc: teaching AI to generate repositories and integration tests in Go - Michelangelo - 09:30

Beyond sqlc: teaching AI to generate repositories and integration tests in Go

Using sqlc (a SQL compiler) in Go is great for generating type-safe database access. However, using the structs generated by sqlc directly in the business logic layer would tightly couple the domain to the database schema. To keep things flexible, a repository layer is needed to map between generated structs and domain models. But writing this mapping by hand quickly becomes repetitive.

The talk begins with a review of the evolution of working with Postgres in Go: from the standard library’s database/sql, through Squirrel + sqlx, pgx driver and API, to modern SQL-first approaches with sqlc. It then introduces an AI-powered generator that creates repository layers based on provided interfaces and domain models, leveraging sqlc-generated queries. The generator, playfully named sqlc++, is powered by Claude Code with a custom command and uses a GitHub project with a reference implementation as an example. This reference-based approach reduces LLM hallucinations and ensures consistent code style.

This approach makes it easier to bootstrap a new repository: in addition to what sqlc already requires (SQL schema and queries), one has to define domain models and a repository interface. From there, the sqlc++ generator produces the repository implementation and integration tests, ensuring consistency and avoiding repetitive boilerplate.

LEVEL: Intermediate

Speaker

Nikolay Kuznetsov

Senior SW Engineer @ Zalando

Place

Michelangelo

Length

45 min

When

October 7th, 2025

09:30

Beyond sqlc: teaching AI to generate repositories and integration tests in Go - Michelangelo - 09:30

Abstract

Using sqlc in Go is great for generating type-safe database access — but using its generated models / records directly in your service layer ties your domain to the database structure. To keep things flexible, a repository layer is still needed to handle mapping and transactions — but writing that by hand gets repetitive fast.

In this talk, I’ll show how I built a custom Go generator that uses AI to create repositories and integration tests on top of sqlc, based on reference implementation I provide. Unlike Copilot Chat, there’s no copy-pasting or re-explaining — the tool understands the structure I want and generates consistent code automatically.

This makes it easy to bootstrap a new project: define your SQL schema, write your queries, repository interface, run sqlc, and let the generator build the repository implementation and integration tests for it.

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