How to resume an AI coding session without starting over
A short handoff of goals, decisions, verified results, and next actions can make the next AI session useful from the start.
A short handoff of goals, decisions, verified results, and next actions can make the next AI session useful from the start.
Give planning, implementation, and review clear responsibilities and handoff criteria when using two AI coding tools.
Compare an AI completion report with actual behavior across code changes, data persistence, errors, permissions, and tests.
Keep the purpose, reason for each revision, and verified result with a reusable prompt instead of saving only its wording.
Use per-version review decisions and reasons to know which document or code artifact is ready to reuse.
Find saved artifacts and prompts by date, project, and AI service instead of searching separate conversation histories.
Choose the decisions, artifacts, and prompts a future session will need instead of collecting every message.
Understand MCP as the connection that lets an AI use your tools, then make a first Wellnote save without memorizing setup terms.
Avoid repeating the same fix by recording the symptom, change, observed result, and next experiment.
Check sign-in, stored data, mobile use, errors, access controls, and recovery before opening an app to real users.
A developer survey, a productivity experiment, agent-authored pull requests, and an incident show where AI-assisted development gets stuck.
When multiple AI tools work in sequence, review drafts and code changes before each handoff. Here is a practical workflow and checklist.