Tests and documentation are things that I'm increasingly using LLMs for. Writing exhaustive tests and documentation is somewhat tedious work. You do it to help others. Or if you are smart enough to realize it, your future self. But LLMs take the tedium away.

Example: I wrote a little fast api endpoint and realized that having some decent openapi documentation would be nice. Support for that is built in. So, copy paste into chat gpt, "add openapi documentation to these endpoints" paste it back. And then "write me an integration test that exercises these endpoints and tests all the exceptional responses". Simple stuff. But the point is that the generated documentation is pretty good and helpful. It's exhaustive. It documents all the essentials. I could sit down and do it manually for an hour. Or I could just generate it.

I also generated a README for the same project with instructions on how to setup all the tools and do all the key things (run tests, run a dev server, build a docker container, etc. The Dockerfile was generated too. Dockerfiles are also great as documentation artifacts because it precisely defines how to build and run your software.

LLMs are really good at documenting and summarizing things.