AI Tools That Accelerate Coding

Pattern: prompt → verify → commit

I always begin with a structured prompt that includes architecture context, acceptance criteria, and non-negotiables like accessibility or performance budgets. After the AI outputs code, I run it locally, add missing tests, and summarize why it’s trustworthy before pushing.

My current stack

For exploratory refactors I lean on GitHub Copilot Chat and continue in VS Code. For API stitching, I prefer Cursor AI because it keeps tabs on diff context in large repos. When I need domain knowledge, I use custom GPTs fine-tuned on our design system and backend protocols.

• ▹Copilot: inline completions + chat for micro-refactors
• ▹Cursor: multi-file edits and auto-applied patches
• ▹Sourcegraph Cody: semantic repo search when onboarding

Metrics that prove impact

I track PR turnaround time, test coverage drift, and defect density to ensure AI isn’t creating cleanup work. On average we reclaim ~8 hours per developer each sprint while maintaining our quality bar.