How to succeed with AI-assisted coding (vibecoding) on Sharetribe

Once you've started vibecoding on top of Sharetribe, the difference between a frustrating experience and a productive one often comes down to working habits rather than the AI tool you use. This article collects practices that make AI-assisted coding more reliable: how to give your AI the right knowledge, how to phrase requests, how to catch mistakes before your users do, and how to recover when something breaks.

This article is right for you especially if you’ve decided to start vibecoding, or are in the process already. Ideally, you’ve already gone through our tutorial on how to start AI-assisted coding on top of Sharetribe, and our what you can and can’t do with vibecoding article.

If your AI tool supports MCP servers, connect it to the Sharetribe documentation MCP server. MCP is a way for an AI tool to search an external source of information when it needs help.

The Sharetribe documentation MCP server searches the Developer Documentation, API and SDK references, Developer Blog, selected Help Center articles, and Sharetribe’s relevant GitHub repositories. This gives the AI access to current Sharetribe-specific information without requiring you to paste all of it into the conversation.

The Sharetribe Development Plugin introduced in our setup tutorial and the documentation MCP server complement each other. The plugin guides the AI through common development workflows, while the MCP server gives it a larger technical reference to search.

If your tool does not support MCP servers, another option is to make the documentation available as local files. The source files for the Sharetribe Developer Documentation are available on GitHub.

You can download the documentation into a separate reference folder and use it as a source folder for your project in, for example, Claude Code or ChatGPT Work/Codex environments, allowing it to consult it when needed. Make sure the AI understands that these are reference files and that it should not edit them as part of your marketplace.

You usually do not need the AI to read every documentation file at once. If you give it the documentation, it should be able to find and read the sections relevant to the feature you are currently building.

When you already know that a relevant guide exists, give the AI the link directly. For example, if you want to add a step to the listing creation flow, link to the relevant tutorial and ask the AI to follow the pattern used there.

This is especially helpful with features related to transactions, pricing, authentication, or extended data, where a generic web-development solution may not be appropriate.

Try to use the same terms that Sharetribe uses. You can read more from our article on technical terms and definitions. For example:

Using the right vocabulary makes it easier for the AI to find the correct documentation and understand which part of Sharetribe you mean.

For example, instead of saying “change the purchasing system,” explain whether you want to redesign the checkout page, change how pricing is calculated, or modify the transaction process. These are different changes with different levels of complexity and risk.

As a non-developer, you do not need to tell the AI exactly how to write the code. In fact, prescribing a technical solution can accidentally send it down the wrong path, if it’s not the appropriate solution.

Start by describing the result you want: who the feature is for, where they should see it, what they should be able to do, and what a successful result looks like. The AI can then inspect the existing code and propose an implementation.

For example: “I want providers to see a preview of their listing card before they publish a listing. The preview should use the information they have entered so far and work on both desktop and mobile. First inspect the current listing creation flow and relevant Sharetribe documentation. Ask me any questions you need, and then propose the simplest implementation. Do not change the transaction process or any Console configuration.”

Screenshots, sketches, and examples are often more useful than a long visual description. You can give the AI:

When sharing screenshots, remove or hide real names, email addresses, payment information, and other private user data.

For visual changes, ask the AI to compare its result with the reference after implementing it. You may still need several rounds of adjustment: phrases such as “make it cleaner” or “make it more modern” can mean different things to you and the AI, so do your best to use precise vocabulary.

If the feature is not completely clear in your own mind, ask the AI to gather requirements before it writes code. For example:

"Ask me up to five questions that would help you understand the intended outcome. Use your built-in multiple-choice question feature when suitable. Do not make any code changes until I have answered."

Useful questions might cover which users should see the feature, where it belongs, how it should behave on mobile, which information it uses, and what should happen when information is missing.

Answering a few questions at the beginning is usually faster than correcting a misunderstood feature after it has been built.

Large prompts often lead to large changes, and large changes are harder to understand, test, and undo. Start with the minimum version that would make the feature useful.

For example, if your eventual goal includes a new dashboard with several reports, filters, exports, and notifications, start with one report displaying the correct information. Test it, save the working version, and then add the next capability.

This also helps you find out early whether the feature is technically more complicated than expected.

Tell the AI which parts of the marketplace are outside the task. This reduces unrelated changes and makes the result easier to review.

For example: "Redesign the listing page, but do not change the top bar, checkout flow, transaction process, or how listing data is saved. Do not add new dependencies unless they are necessary, and explain why before adding one."

If there is existing behavior you depend on, mention that too. Something that looks unnecessary to the AI may support a user type, mobile layout, or edge case that is not immediately visible.

AI-assisted coding involves experimentation. Some experiments will work, and some will not. Version control gives you safe checkpoints so that one unsuccessful change does not undo everything that came before it.

Git keeps a history of changes to your project, while GitHub stores that history online and provides a backup. If you followed our setup tutorial, you may already have a GitHub repository for your marketplace.

Your AI coding tool can usually handle version control for you. You can ask it to show you what changed, save a working version, or return to an earlier one. Still, ask it to explain the action in plain language before allowing it to undo or overwrite changes.

Consider using a private GitHub repository unless you have deliberately decided to make your marketplace code public. Most importantly, never commit your .env file, Sharetribe client secret, Stripe secret keys, or other credentials. Ask the AI to verify that secret files are excluded from Git before it uploads anything.

A commit is a named checkpoint in the project history. Create one (or ask your AI to create one) after each feature or logical change that works—not only at the end of a long session.

A useful rhythm is:

This makes it much easier to identify what caused a problem. It also lets you return to the last working version without losing unrelated progress.

Ask for commit messages that you can understand later, such as “Add listing preview to creation flow” rather than “Update components.”

AI sessions do not always share the same conversation history. A few small documentation files can help a new session understand the project without reconstructing every decision.

For example:

You do not need to document every small visual adjustment, though the AI can also do that for you if you wish. However, you may also want to focus on decisions that a future AI session, or a developer, would need to understand before changing the feature.

At the end of a session, you can ask: “Update the project documentation with what we completed, the decisions we made, the tests that were run, any known problems, and the recommended next step. Keep it concise and do not include secrets.”

Branches are an optional, more advanced way to try changes without affecting the main working version on GitHub. You can ask the AI to create a branch before attempting an uncertain or extensive feature.

If the experiment works, it can be merged into your main version. If it does not, the branch can be abandoned while your working marketplace remains intact. Ask the AI to confirm which branch it is using before it starts making changes.

After building a feature, open a fresh session and ask another AI to review it. If possible, use a different model or even a different model provider. For example, if you used Anthropic’s Claude to create the customizations, you may want to use OpenAI’s ChatGPT to analyze them - or just a different model from the same AI provider, as long as it is a fresh session. A fresh session has not participated in the implementation and is less likely to defend earlier decisions. Its job is to look for weaknesses, not to continue building.

Ask the auditor not to edit the code immediately. It should first produce a report that you can review and take back to the original building session.

For example: “You are in a directory containing a customized Sharetribe Web Template. Adversarially audit the changes in this project. Do not edit the code. Look for functional bugs, regressions, security and privacy issues, incorrect use of Sharetribe APIs, browser-side handling of secrets or trusted values, problems on mobile, missing error states, and untested edge cases. Pay special attention to payments, authentication, private data, and the transaction process.

Verify each finding against the code and current Sharetribe documentation. Rank the findings by severity, explain the evidence, and include steps to reproduce the problem where possible. Ignore purely stylistic preferences. Save the report as a Markdown file whose name includes today’s date.”

Take the findings back to the building session and ask it to verify them. AI auditors can also make mistakes, so a finding should not automatically lead to a code change. The building session should explain whether the issue is real, how it was confirmed, and how the proposed fix avoids creating a new problem.

An AI audit raises the quality floor, but it does not replace expert review. This is especially true when your changes affect payments, authentication, private user data, or the transaction process. If the risks are significant, consider hiring a Sharetribe Expert to implement or review the feature.

The fact that a feature looks correct on one page does not mean it works throughout the marketplace. AI-generated code can miss situations that were not mentioned in the original prompt, so manual testing remains essential.

Do your development in the Dev environment, using test users and test payment details. Keep your Dev configuration representative of the marketplace you plan to launch. You can learn more about the different environments in this Help Center article.

Test the marketplace while logged out and as every customer and provider user type your marketplace uses. This is particularly important when different user types have different signup steps, listing fields, permissions, or transaction experiences.

Do not only test with the account you used while building the feature. A provider may see a perfectly working page while a customer sees an error—or vice versa.

Also test on both desktop and mobile. Check narrow browser windows as well as a real phone when possible.

If your changes touch checkout or transactions, test the entire process rather than only the screen you edited. Depending on your transaction process, this can include:

Check that the correct actions are available to the correct user at every stage. Also confirm that marketplace emails and Console show the expected information.

Real users do not always follow the happy path. Try empty, unusually long, invalid, and unexpected values.

For example:

Ask the AI to create a testing checklist for the specific feature, but do not rely only on tests performed by the AI. Automated checks are useful, and the Web Template includes a testing setup described in the testing and continuous integration guide, but automated tests do not perfectly reproduce real browser and user behavior.

Finally, ask someone who did not build the feature to try it. Fresh eyes tend to find assumptions that both you and the AI have stopped noticing.

AI coding tools may charge based on usage or limit how much you can use a model during a certain period. Long conversations, large text files, repeated failed attempts, and broad requests can all increase usage.

Quick visual adjustments, small text changes, and straightforward investigation may not require the most capable model available. Multi-file features, difficult debugging, architectural decisions, and audits are better uses for stronger models.

If your tool lets you switch models, you can use a faster or less expensive model for routine work and a more capable one for complex or high-risk tasks. But the cheapest attempt is not always the cheapest overall: a weak model repeating an incorrect approach can cost more than one careful pass with a stronger model.

Point the AI to the feature, page, or files involved instead of asking it to inspect the entire codebase every time. Ask it to search the Sharetribe documentation for the specific question rather than specifically loading all documentation into the conversation (though you can still use the documentation as a reference/source folder for the project in your coding environment as mentioned in Section 1: this does not automatically load all of it in the conversation).

At the same time, do not restrict it so tightly that it cannot inspect related code. A listing creation change, for example, may also affect how the listing is edited and displayed. Ask the AI to identify the relevant files first, then agree on the scope.

Large configuration and marketplace text files can also consume a lot of credits. Before editing one through AI, check whether the same change can be made more easily in Console.

A very long session can accumulate outdated instructions, abandoned ideas, and large amounts of code. If the AI starts confusing the current requirement with an earlier one, save the working changes, update your project notes, and begin a fresh session with a concise summary. If you want, you can also ask the earlier conversation for a handoff prompt to the new one, though sometimes it is also a better idea to just start fresh.

A clean session with good project documentation is often more efficient than repeatedly correcting a confused one.

Vibecoding can make experimentation quick, but it can also make it tempting to keep prompting long after progress has stopped.

Write down a small set of acceptance criteria before building a feature. For example:

Once those conditions are met and tested, save the version and stop. Additional ideas can become separate features with their own plans and checkpoints.

Decide in advance how much focused time you will spend on a session. Coding, including AI-assisted coding, requires attention, and repeatedly trying slightly different prompts while tired often makes the project harder to understand.

If you are no longer making progress, pause. Save or revert the current changes, write down the problem, and return later with a fresh session. You can also ask a second AI to diagnose the issue without editing anything.

A useful stopping point is when the AI has attempted the same kind of fix several times without being able to explain the underlying problem.

Sometimes custom coding is more complex than expected. In other cases, the feature may work, but you may not feel confident releasing it—especially if it handles payments, authentication, identity, private user data, or a custom transaction process.

You do not need to hand over your entire marketplace. A Sharetribe Expert can review a specific feature, fix one difficult part, or help you decide whether the implementation is safe to launch. You can find vetted developers in the Sharetribe Expert Network.

Thanks for reading through this article that covered a lot of tips and tricks on vibecoding! Remember to check out our other articles on vibecoding, including How to start AI-assisted coding on top of Sharetribe for non-developers and What you can and can't do with AI-Assisted coding (Vibecoding).

Note that none of these tricks make vibecoding completely risk-free: they merely increase your chances at success, and make mistakes cheaper, earlier, and easier to see.