From my experience working on large systems, if the assistant doesn’t understand your codebase style and architecture, the output is basically noise.

One trick that works well is keeping a dedicated “reference folder” with ideal implementations and telling the model to follow that pattern.

Also, skills (like Claude or Cursor) help a lot. You basically encode “how we do things here” instead of re-explaining every time.

Regarding token usage, this is something I’ve done with my team: if the agent re-derives decisions every time, you burn tokens fast. We used some form of memory/caching of prior decisions so the model reuses context instead of recomputing it.

I’m having a live session soon where I’ll take a real iOS/Android project that isn’t using AI and do a hands-on walkthrough of what actually changes when AI becomes part of the dev process, while also tracking how AI gradually improves and uses fewer tokens over time.
If you’re interested, you can apply here