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Richard Chai
Richard Chai

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Debugging Smarter and Feature Iteration with Trae AI

#ai #productivity #softwaredevelopment #tutorial

In this third and final post of the series “Turn Ideas into MVP Fast with Trae AI,” let's look at how to debug issues and add features to the AI Generated App.

In this post, we’ll cover:

  1. Why “Please Fix It” Prompts Are Expensive
  2. Best Practices for Debugging Effectively with Trae.ai
  3. Adding Features to Your AI-Generated App

1. Why "Please Fix It" Prompts Are Expensive

When you hit a bug, telling AI, “Please fix it” may seem quick, but it’s often costly, not just in money but also in time and potential technical debt. This is because vague prompts force the AI to guess, which can lead to broad or unnecessary changes that don’t address the root cause.

The resulting inefficiencies typically fall into several categories:

1.1 Money Talks (Token Usage)
Most large language models, like those from OpenAI, Anthropic, or Google, charge based on how many tokens they process - both your input and the response.

A vague “please fix it” prompt doesn’t give the AI anything concrete to work with, so debugging turns into a guessing game where the AI has to make many assumptions. This results in the AI refactoring unrelated code, tweaking style, or adding libraries you don’t need instead of fixing the real problem. That adds up fast in both time and token cost.

1.2 The “Context Dump” Problem
To cover all bases, some developers dump huge chunks of code into the prompt. While it seems thorough, it’s inefficient because the AI has to sift through a lot of irrelevant code, which slows things down, drives up costs, and increases the chance that it focuses on the wrong part of your app.

1.3. New Bugs and Technical Debt
Broad or speculative fixes can have unintended consequences. If the AI rewrites parts of the code without a clear understanding of the problem, it might break unrelated components or introduce inconsistent patterns.

Even if the immediate error is fixed, you could end up with messy, harder-to-maintain code that increases technical debt over time.

2. Ten Best Practices for Effective Debugging

Debugging with AI app generators or coding assistants works best when you treat the AI less like a “magic fixer” and more like a capable collaborator that needs structured input.

The goal is to reduce ambiguity, isolate the problem, and provide the model with enough context to reason about the issue without overwhelming it with irrelevant information.

Several practical techniques can significantly improve debugging outcomes.

2.1 Isolate the Problem Before Prompting
One of the most effective steps is narrowing the scope of the issue before asking the AI for help.

Instead of having the AI read your entire application code, identify the affected:

  • Specific file, module, screen, or web page.
  • Function or component involved.
  • Line or block where the error occurs.
  • Conditions that trigger the bug.

2.2 Provide the steps to reproduce the issue
AI systems work more effectively when they are informed of the circumstances under which the bug occurs.

You should include:

  • Input values or request payloads
  • User actions or workflow steps
  • Environment conditions (browser, framework version, API response)

2.3 Include the Error Message and Stack Trace
Error messages are among the most valuable debugging signals, often containing precise information about the failure point and execution path.

Best practice:

  • Provide the exact error message
  • Include the stack trace.
  • Avoid paraphrasing.

2.4 Provide Only Relevant Code Context
Context is essential, but too much can reduce the AI’s accuracy.

Instead of dumping entire files or repositories:

  • Share the specific function.
  • Include dependent helper functions.
  • Include relevant configuration if necessary.

This keeps the AI focused and preserves context window capacity.

2.5 Define the Expected Behavior
Many debugging prompts fail because they explain what is broken, but not what should happen instead.

Include a clear success condition, such as:

  • "The function should return a validation error instead of crashing."
  • "If email is missing, the API should return HTTP 400."
  • "The UI should display a fallback message instead of rendering a blank page."

2.6 Ask for a Diagnosis Before Requesting a Fix
Jumping directly to asking the AI to “fix the code” can lead to unnecessary changes.

A more effective strategy is to prompt in stages:

  1. Diagnosis prompt
    "What is the root cause of this error?"

  2. Fix prompt
    "Suggest the minimal code change to resolve it."

  3. Validation prompt
    "Are there any edge cases this fix might introduce?"

This workflow mirrors professional debugging practices and helps produce more reliable solutions.

2.7 Request Minimal Patches Instead of Full Rewrites
AI models sometimes attempt large refactors when a small change would suffice.

To prevent this, explicitly constrain the response:

  • Provide a minimal patch.
  • Do not refactor unrelated code.
  • Return only the modified function.

This reduces the risk of introducing new bugs or architectural inconsistencies.

2.8 Use Iterative Debugging Instead of One Large Prompt
Effective AI-assisted debugging is iterative.

A typical workflow might look like:

  1. Identify the failing component
  2. Ask the AI to analyse the error
  3. Test the proposed fix
  4. Provide feedback or new logs
  5. Refine the solution

Short, focused iterations almost always outperform large, unfocused prompts.

2.9 Validate the AI’s Fix
Even powerful models can generate incorrect solutions. Always verify the proposed changes by:

  • Running tests
  • Checking edge cases
  • Reviewing for architectural consistency
  • Confirming performance or security implications.

Treat AI output as a proposed patch, not an authoritative answer.

2.10 Use Structured Debugging Prompts
Over time, teams benefit from standardizing their debugging prompts.

One useful template is the “Given–When–Then–But” format from Behaviour-Driven Development (BDD).

Like a developer, the AI needs three key pieces of information to diagnose and fix a bug efficiently:

  1. Context: What’s the state of the system when the bug appears?
  2. Action: What triggers the bug?
  3. Expected vs. Actual Outcome: What should happen, and what actually happens?

The Given-When-Then structure captures these elements in a concise and predictable format. Adding “And” and “But” allows you to specify additional conditions or exceptions without breaking the flow. Example:

❌ Vague Prompt (Expensive):
"The login button doesn't work. Just fix it. "

The AI now has to guess: Is it a front-end issue? Back-end? Does it throw an error? What browser? What user? This leads to a long, token-wasting conversation.

✅ Structured Prompt (Efficient) :
Given a registered user with the email "test@example.com" and password "secret,"
And the user is on the login page,
When they enter correct credentials and click the "Login" button,
Then the page should redirect to the dashboard.
But currently, the page just reloads and shows no error.

This prompt tells the AI:

  • Exact scenario (user, page, actions)
  • Expected behaviour (redirect to dashboard)
  • Actual bug (page reloads, no error)

The AI can now zero in on possible causes (e.g., JavaScript form submission preventing default, API call not firing, response not handled correctly). It doesn't need to ask for clarification, so the fix will be quicker, cheaper, and more accurate.

Trae makes implementing these best practices easy:

3. Adding Features to Your AI-Generated App

Once your app runs smoothly, the next step is usually adding features, and vague prompts like “add X” can cause messy code, broken workflows, or unnecessary rewrites.

When asking AI to add features:

  • Define the feature clearly

    • What it does
    • The problem it solves
    • Constraints or limits

  • Provide technical context

    • Framework / tech stack
    • Relevant files or modules
    • Where it should connect (API endpoints, UI components, DB tables, jobs)

  • Work in incremental steps

    • Design architecture
    • Update schema
    • Implement backend logic
    • Add endpoints
    • Build frontend

  • Control the output

    • Return only needed changes (e.g., modified functions, patch diff)
    • Follow project standards (naming, structure, error handling, logging)

  • Ensure reliability

    • Define success criteria and edge cases
    • Protect existing functions/modules
    • Request a self-review for regressions, performance, security
    • Validate with tests, example API calls, or usage scenarios

Key principle: clear instructions + small steps + strict boundaries = safer feature additions without breaking existing code.

You can do all of these manually.

Or you could let Spec-driven development in TRAE SOLO help you.

It's a workflow that gives your agent a persistent implementation plan to follow, so it always knows where it is and where it's going.

4. Your Turn

  1. Pick a bug in your current project and use the best practices (above).
  2. Add a feature using Trae Solo spec-driven development.
  3. Compare the results with your usual workflow.

The more intentionally you use AI tools like TRAE AI, the more they shift from “code generators” to true development partners that help you debug faster, iterate safely, and ship features with greater confidence.

If this guide helped you refine your workflow, consider sharing your experience or your own prompt strategies in the comments, your insights might help the next developer build smarter with AI.

This is the final post in a 3 part series: Turn Ideas into MVP Fast with Trae AI
1. Stop Guessing: Turn Vibe Coding from "Sometimes Magic" to "Reliably Powerful"!
2. Visual Customization with Natural Language Prompts in Trae.ai
3. Debugging Smarter and Feature Iteration with Trae AI

Here's the link to my 10x AI Engineer, Trae: https://www.trae.ai/.

To learn how to use Trae.ai: https://www.youtube.com/playlist?list=PLvDpQQwPxoz2nI9WyErHjntvrrQsf1Kp8

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