One example of boilerplate that I've been automating is when you're creating model code for your ORM.
I paste the table definition into a comment, and let the LLM generate the model (if the ORM doesn't automate it), the list of validation rules, custom type casts, whatever specifics your project has. None of it is new or technically challenging, it's just autocompleting stuff I was going to write anyway.
It's not that you're writing "too much" boilerplate; this is a tiny part of my work as well. This is just the one part where I've actually found an LLM useful. Any time I feel like "yeah this doesn't require thought, just needs doing", I chuck it over to an LLM to do.
I've found this very useful as well. My typical workflow (server-side kotlin + spring) has been:
- create migration files locally, run statements against containerized local postgres instance
- use a custom data extractor script in IntelliJ's data tool to generate r2dbc DAO files with a commented out CSV table containing column_name, data_type, kotlin_type, is_nullable as headers
- let AI assistant handle the rest
I paste the table definition into a comment, and let the LLM generate the model (if the ORM doesn't automate it), the list of validation rules, custom type casts, whatever specifics your project has. None of it is new or technically challenging, it's just autocompleting stuff I was going to write anyway.
It's not that you're writing "too much" boilerplate; this is a tiny part of my work as well. This is just the one part where I've actually found an LLM useful. Any time I feel like "yeah this doesn't require thought, just needs doing", I chuck it over to an LLM to do.