My AI Builder Project Is Stuck. What Now?

Stop prompting, go back to the last version that worked, and work out the actual error before asking the AI again. Most stuck projects are stuck for one of three reasons: the AI is in a fix loop, the project has grown too large for the AI to keep in view, or you have run out of credits. Each one has a different way out, and sometimes the right move is to take the code out of the builder.

Match your situation to one of these before doing anything else:

• Fix loop: the same error comes back after every "fixed it" reply, or each fix breaks something that used to work.
• Too big to hold: the AI forgets earlier decisions, rewrites files you didn't ask about, replies slowly, or edits get worse as the project grows.
• Out of budget: you hit a daily or monthly limit with the feature half built.
• Beyond the builder: you need something the tool doesn't do well, like a background job, a complex integration, or a native mobile build, and every attempt produces a workaround that half works.

The sections below take these in order. Whatever your case, do the next step first.

A stuck project usually has several bad edits stacked on top of the original problem. Undoing them gives you a clean starting point and stops credits draining while you think.

• Lovable: the FAQ's guidance is to revert to an earlier version when a recent change broke something.
• Replit: pick a checkpoint in the Agent tab and choose "Rollback to here". If the bad edits also changed data, tick "Database" under the additional rollback options, because the development database is not rolled back by default.
• Bolt: Bolt's own advice is to download your project as a backup before cleaning up or refactoring, which also gives you a fallback copy.
• Claude Code: /rewind (or Esc twice) restores code, conversation, or both to an earlier prompt.

If you are not sure which version was the last good one, open two or three older versions and test the specific feature in each. Write down which one works. That is your baseline.

A fix loop happens when the AI is guessing. It sees a symptom, changes something plausible, and the real cause stays. More attempts with the same information produce more guesses.

Lovable's docs suggest using Try to fix "once or twice, then investigate": switch to Chat or Plan mode, describe what you see and what you expected, and ask for an investigation before any code changes. The same approach works in any tool. Ask the AI to explain what it thinks causes the bug and which files are involved, and do not let it edit until the explanation makes sense to you. Also note the cost: Lovable gives 10 free fixes shared between Try to fix and Security view fixes, each coming back 24 hours after use, and after that Try to fix uses credits like a normal build.

Then give it better evidence than "it still doesn't work":

• The exact error text from the browser console, or the failed request and its status code from the Network tab.
• The build log if the error happens on deploy.
• The database or edge function logs if the error comes from the backend. In Supabase these are in the dashboard's logs section.
• What you clicked, what you expected, and what happened instead.

If the error message mentions a permission or policy, an empty result, or a 401, the bug is probably configuration (auth settings, Row Level Security, an API key) rather than code. No amount of code edits will fix a missing setting, which is a classic reason for loops. Our symptom checklist covers those causes.

Once you are back on a working version and have the real error in hand, a structured prompt gets far better results than another "please fix". This template works in Lovable, Bolt, Replit, v0, Cursor and Claude Code:

• Goal: one sentence on what the feature should do, from the user's side. "A signed-in user can upload a profile photo and see it on the dashboard."
• Current behaviour: what happens instead, step by step.
• Evidence: the exact error text, the failed request and its status code, and the relevant log lines.
• What you already tried: so the AI doesn't suggest it again.
• Boundaries: which files or features it must not change. "Do not touch the auth flow or the database schema."
• Ask for a plan first: "Explain the likely cause and the change you would make. Don't edit anything yet."

Read the plan. If it names a cause you can check, check it. If it proposes rewriting half the app to fix one button, say no and ask for the smallest change that would test its theory. After the fix, test the feature yourself and save the version before moving on.

Every builder works inside a context window, the amount of your project and chat the model can consider at once. Bolt's documentation says that when context gets too large, you might see errors, slower responses, or lower-quality results, and that most token usage comes from Bolt reading and syncing your project files, so larger projects use more tokens per message.

What helps, in Bolt and elsewhere:

• Clear the chat context. In Bolt, type /clear in the chatbox and click Clear context. In other tools, start a new chat and paste a short summary of the project and the current task.
• Break up large files. Bolt recommends splitting large files into smaller ones, because files with thousands of lines fill the context quickly. A 1,500-line page component is also harder for you to review.
• Ask for one change per prompt. "Add the settings page, fix the login bug and change the colours" invites the model to touch everything. Three prompts, each tested, go further.
• Keep a written project brief. Builders that support project-level instructions let you record the stack, the data model and the rules once, instead of hoping the AI remembers them. In code editors, Cursor reads rules from .cursor/rules and AGENTS.md, and Claude Code reads a CLAUDE.md file at the start of every session.

Know your limits before a big change, not during it. On Lovable's Free plan, build credits are 5 per day, up to 30 per month; daily build credits don't roll over, while unused monthly plan credits on paid plans roll over for a limited time. Bolt's Free plan has a 300K daily cap and 1 million tokens a month, and free tokens don't roll over.

Ways to keep going without spending more:

• Edit by hand for small things. Bolt notes that direct edits in Code view don't use tokens, so text changes, colours and obvious one-line fixes are cheaper to make yourself.
• Plan before you build. Use a planning or chat mode to agree the approach, then ask for the change in one clear prompt instead of five vague ones.
• Stop at a working state. If you are near the limit, finish on a version that works and save or bookmark it, rather than starting a big feature you can't complete until tomorrow.

If you hit the cap every day, compare plans on our pricing page, or consider moving the heavy editing to a code editor, as below.

Moving out makes sense when the builder keeps fighting you on one specific thing, when you want to see and control every change, or when credits per edit have become the main cost. It does not make sense if you cannot read any code at all and have no one to help, because the editor will show you more code, not less.

The path is usually through GitHub. Lovable's GitHub sync is available on all plans and works both ways: changes in Lovable push to GitHub, and commits pushed to the synced branch come back into Lovable. It syncs one branch at a time, by default the repository's default branch. That means you can open the repository in Cursor or Claude Code, make a hard change there, push it, and keep using Lovable for the visual work.

Before you start, ask the AI in the editor to read the project and write a short summary of how it fits together. Save it as the rules file the editor reads (AGENTS.md, .cursor/rules or CLAUDE.md). If your builder hosts the backend for you, as Lovable Cloud and Base44 do, check what can actually leave the platform (code, database, users, secrets) before you plan the move.

Sometimes rebuilding is cheaper than rescuing. Consider it when:

• You can't find any version that works, even after going back several steps.
• The data model is wrong at the root, for example users and accounts mixed together, and every feature works around it.
• The project grew from many unrelated experiments and nobody, including the AI, can explain how it fits together.

A restart goes faster the second time. Write a one-page spec first: the screens, the data each one needs, who can see what, and what "done" means for the first version. Start the new project from that spec, keep the old one as a reference for copy and design, and commit or bookmark after each feature that works. The second build tends to go faster because the hard part, deciding what the app should do, is already on paper.