Trae AI Setup Checklist Before Your First Build

You open a project in Trae AI, describe a feature, and get a promising change. Before sending the next prompt, check whether you can see what changed and recover if the result breaks something else. Trae AI is an AI-assisted coding tool, but its exact modes and controls can change, so verify the current options in its documentation. Your setup should give you a clear way to review work regardless of which mode you choose.

Use these checks when starting a project or moving an existing one into an AI-assisted workflow. Each item should have an answer you can point to in the project, rather than an intention you hope to remember.

Before you let Trae AI edit

• Can you run the project yourself? Open the app and confirm its main screen works before changing code. That gives you a baseline for the next result.
• Can you inspect changed files? Find the project's diff or version history. If you cannot see which files changed, pause before asking for a broad refactor.
• Have you set a task boundary? Name the feature and the files or behavior that should stay untouched. “Fix the invite form without changing authentication” is easier to review than “clean up onboarding.”
• Are secrets outside the prompt? Keep credentials in the appropriate environment configuration. Replace private values in any logs you share while debugging.

A small feature is a useful first task because its result is easy to test. If you are bringing in an existing app, identify the working behavior you want to preserve before requesting changes. That makes a surprising edit visible instead of leaving you to discover it after several more prompts.

Before you accept a change

• Do you understand the diff? Ask about unfamiliar edits, then read the affected code or configuration. The agent's summary is a starting point for review.
• Does the intended flow work? Try the action you asked for in the running app, including an invalid input or failure case.
• Did permissions or data behavior change? Review auth checks, database writes, and delete actions closely. Test with safe data and an account that should have limited access.
• Can you undo the result? Keep a known good revision before layering another change on top of one you have not verified.

Checking one change at a time keeps debugging manageable. When the result fails, tell the tool what you did, what happened, and what you expected. That gives the next attempt a concrete target without asking it to reshape the entire project.

Before you end the session

• Is the current state written down? Note what works and what still fails. Include the next action you would take when you reopen the project.
• Did a prompt earn a place outside chat? Save a prompt that solved a hard issue with the result it produced and a note on when to reuse it.
• Should a quick todo become a feature? Move a larger idea out of today's notes so it does not compete with the immediate fix.

This final pass is short, but it changes how the next session begins. You can return to a decision and a next action instead of rereading a long conversation to work out where you left off. That is valuable whether you continue in Trae AI or open the same code elsewhere.

If you switch tools, this six-step guide to tracking project state across AI coding tools shows how to carry notes, decisions, and next actions with you.

Give the project a place to resume

Before you close the editor, leave one clear next action. A note such as “Test invite rejection with a non-owner account” is more useful than “finish invites.” Keep the prompt that fixed the last difficult issue nearby too, with enough context to tell whether it applies again.

Keep your build notes and reusable prompts in VibeCrumbs so your next session starts from the state you already reached.