A Practical Guide to AI Assisted Software Development

Where AI helps

AI is useful for exploring an unfamiliar codebase, sketching tests, explaining an error, and comparing implementation options. It is less reliable as an unchecked author of production changes. The 2025 DORA research describes AI as an amplifier of the surrounding engineering system: strong practices benefit, while weak practices become more visible.

A workflow that keeps quality visible

Start with a small, testable task and state the acceptance criteria. Ask the assistant for an approach and the assumptions behind it. Review the proposed change against the actual code, interfaces, and security requirements. Run tests and inspect the diff yourself. Keep the pull request small enough for a colleague to understand. When the tool invents an API or misunderstands a requirement, correct the context and verify again.

For consequential work, ask for alternatives and tradeoffs rather than a single answer. Require evidence for unfamiliar claims and consult the official documentation. Never paste secrets or private customer data into a tool without an approved data policy.

Measure outcomes, not generated lines

Track review time, rework, escaped defects, change size, and developer experience alongside delivery speed. A faster first draft is not a win if it creates more downstream work. DORA has also reported tensions between faster code creation and delivery stability, which makes verification especially important.

Try this next week

Pick one recurring low-risk task. Write a reusable brief with the repository context and acceptance criteria. Compare a few AI-assisted changes with the usual workflow. Keep the practices that improve the whole path from idea to safe release.