Your team owns the build.
Learn through courses, practical training and workshops. Get feedback as you apply the lessons to your own project.
Explore courses and workshops ↗Connect product intent, engineering context and review evidence so AI-assisted development produces maintainable changes.
Teams can generate code faster than they can settle requirements or inspect the result. Unclear ownership turns that speed into rework.
Build a repeatable path from an agreed problem to a reviewed change, with engineers responsible for the system.
A product owner selects a bounded customer problem for engineering planning.
Prepare a coherent implementation brief, identify unresolved product decisions and map verification needs to the intended behavior.
Product approves scope and an engineer validates the design. Generated code follows the team's normal review and release process.
Confirm repository boundaries, customer-data access and release controls. AI assistance must not turn speculative requirements into approved scope.
Measure clarification cycles, scope changes, reviewer effort and defects discovered after acceptance.
Before expanding the workflow, compare the result with the current process. Include review effort and exceptions in that comparison so the improvement reflects the team's actual experience.
Read the implementation guide ↗The workflow stays centered on your business. The engagement determines who learns, implements and operates it.
Learn through courses, practical training and workshops. Get feedback as you apply the lessons to your own project.
Explore courses and workshops ↗Get everything in DIY, three months of workshops, weekly team sessions and direct implementation coaching.
Explore guided implementation ↗Include all the learning and guidance, with ownership of agreed operations and major implementation decisions.
Explore managed delivery ↗Looking for a course, help with a difficult problem, or someone to build a solution? Tell us where you are and what you'd like to do next.