Software Engineering & AI-Augmented Development
AI is changing developers' daily work. Articles on workflows, patterns and tools for faster and more reliable development.
Augmented development means AI in the service of developers — not the other way around. Test generation, assisted refactoring, automatic documentation, augmented code review: these articles give you concrete workflows to integrate AI into your daily dev work, without sacrificing quality.
11 articles in this cluster
Augmented Code Sprint: engagement model and deliverables
The Augmented Code Sprint is a 2-to-4-week intervention format to integrate AI into a team's development practices. Here is what it contains, what it delivers, and why it is the most effective format to get started.
Measuring dev productivity: what to look at (without vanity metrics)
Lines of code, number of commits, velocity — classic productivity metrics are misleading, especially with AI. Here are the indicators that really measure the impact of AI on your development team.
Augmented backlog grooming: user stories faster, better
Backlog grooming consumes 10 to 15% of sprint time. AI can write the user stories, suggest the acceptance criteria and estimate complexity — provided you do not give it the final say.
Security: avoiding secret leaks with AI
Generative AI in the development workflow creates new vectors for secret leaks: API keys, tokens, credentials sent in prompts. Here is how to secure your practices without slowing down productivity.
Augmented code review: checklist and limits
AI can pre-review your PRs and catch 60% of issues before the human review. But it has critical blind spots. Here is a checklist to combine AI review and human review effectively.
Prompts for developers: pattern libraries (examples)
A good prompt makes the difference between mediocre generated code and production-ready code. Here is a library of tested prompt patterns for developers, with concrete examples for Copilot, Cursor and Claude Code.
Does AI coding really replace developers?
Claude Code, GitHub Copilot, Cursor: AI programming tools are booming. Between promises of automation and the reality on the ground, a breakdown of what AI really changes for development teams.
Assisted refactoring: a safe and reproducible workflow
AI can speed up the refactoring of legacy code, but a poorly framed prompt can introduce more bugs than it fixes. Here is a safe workflow, tested on real projects, to refactor with confidence.
Generating technical documentation without making it wrong
AI can write your technical documentation in minutes — but 40% of the generated content is inaccurate or outdated. Here is how to leverage AI generation while guaranteeing the reliability of your docs.
Accelerating tests: AI-powered test generation + maintenance
Generating unit and integration tests with AI is possible — but without a method, you end up with brittle tests that break on every refactor. Here is a reliable workflow to speed up your coverage without sacrificing maintainability.
Augmented development: what AI really changes (2026)
AI does not replace developers — it transforms the way they work. From assisted code to generated tests, here is what is concretely changing in 2026 for development teams.