Start with a concrete delivery problem
Identify where development slows down: understanding legacy code, drafting repetitive changes, writing documentation or checking an implementation. Choose a bounded task with a clear expected result before introducing AI.
Give AI useful project context
Describe the user need, constraints, architecture and acceptance criteria. Break larger changes into small steps. Use approved project information and avoid putting credentials or confidential customer data into prompts.
Use AI throughout the implementation
AI can help explore options, draft code, explain existing behavior and prepare checks. A developer still decides what belongs in the product, reviews the change and validates it against the actual system.
Keep verification in the delivery process
Review the diff, test the important user journeys and check failure states. Verify generated assumptions about dependencies, permissions and data handling. Keep rollback and deployment decisions explicit.
Measure the result, then expand
Compare time from a defined request to a validated release, alongside defects and rework. A faster first draft is only useful when the delivered result works. Intermedica shows how we apply this approach to a patient platform; its results belong to that project, not a universal speed promise.