Technology
Putting order into how a development team uses AI
Rolling out AI-assisted development workflows across a whole team: tool selection, project-level instruction files, review conventions and training.
- Client
- Digital product team
- Year
- 2024 — present
- Whole team
- on shared criteria, not just the curious ones
- Versioned
- AI instructions live in the repository
- Review
- explicit conventions on what gets accepted
The situation
The team was already using AI tools, but everyone in their own way: different tools, different criteria about what to accept and no shared conventions. The results were uneven — very good in some cases, counterproductive in others — and hard to review.
What was done
- Tool selection based on where the team’s bottleneck actually was.
- Project-level instruction files versioned in the repository, so project context and conventions travel with the code instead of living in each person’s head.
- Review conventions: what a human must always verify, what can be accepted on a quick read, and what should never be generated automatically.
- Hands-on training on the team’s own repository, with written agreements at the end of each block.
What changed
The measurable effect was not "writing code faster", which is the easy promise, but shorter delivery cycles from removing time lost to project set-up, repetitive work and arguments about conventions that were already written down.
Next step
Half an hour well spent
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