AI coding assistants can be genuinely useful, but the value depends on the task, codebase context, and review discipline around generated changes.
A serious review should test repository navigation, edit quality, test creation, debugging behavior, and whether the assistant respects existing patterns.
The best tools make developers faster without making them less attentive.
Best use cases
- Navigating unfamiliar repositories
- Small to medium implementation tasks
- Generating focused tests after a clear behavior change
Strengths
- Can accelerate routine implementation work.
- Useful for explaining code and surfacing likely test cases.
Limitations
- Can confidently produce changes that do not match project conventions.
- Requires human review for architecture, security, and product judgment.