LLM driven coding can yield awesome results, but you will be typing a lot and, as article states, requires already well structured codebase.

I recently started with fresh project, and until I got to the desired structure I only used AI to ask questions or suggestions. I organized and written most of the code.

Once it started to get into the shape that felt semi-permanent to me, I started a lot of queries like:

```

- Look at existing service X at folder services/x

- see how I deploy the service using k8s/services/x

- see how the docker file for service X looks like at services/x/Dockerfile

- now, I started service Y that does [this and that]

- create all that is needed for service Y to be skaffolded and deployed, follow the same pattern as service X

```

And it would go, read existing stuff for X, then generate all of the deployment/monitoring/readme/docker/k8s/helm/skaffold for Y

With zero to none mistakes. Both claude and gemini are more than capable to do such task. I had both of them generate 10-15 files with no errors, with code being able to be deployed right after (of course service will just answer and not do much more than that)

Then, I will take over again for a bit, do some business logic specific to Y, then again leverage AI to fill in missing bits, review, suggest stuff etc.

It might look slow, but it actually cuts most boring and most error prone steps when developing medium to large k8s backed project.