# AI/LLM-driven development

> Using models inside your engineering process — migration rigs, code conversion, review — with the output verified rather than trusted.

Source: https://amitkuzi.com/services/ai-driven-development/

The productive use of an LLM in engineering is rarely "write this feature". It
is the repetitive, mechanical, high-volume work: converting a few hundred
service endpoints, translating a legacy pattern into the current one, drafting
the tests for code that never had any. The value comes from the verification
step around the model, not from the model.

I built exactly this at GreenRoad — an AI-assisted rig that converted legacy
CoreWCF services to REST APIs — and the lesson was that the harness matters
more than the prompt.

## What you get

- The tasks in your pipeline where a model beats a person, and the ones where it does not
- A conversion or generation harness with verification built into it
- Cost per run and failure behaviour measured before it goes wide
- Your team able to run it after I leave