OxCode Blog
How to Debug AI-Generated Code in VS Code Before You Ship It
You prompt an AI assistant to patch an authentication route, paste the generated code into VS Code, and the red squiggly lines vanish. The implementation looks clean. It even has explanatory comments. Ten minutes later, your staging webhook crashes on a null reference or an unhandled Promise rejection because the AI assumed an API response shape that doesn't match reality.
Solo developers, freelancers, and indie builders can't treat AI suggestions as production-ready code. Without a teammate running code reviews, you're the last line of defense. LLMs write code that looks syntactically correct while hiding logical edge cases, outdated package signatures, and made-up assumptions.
Debugging AI output needs a structured, repeatable workflow. This guide shows how to debug AI-generated code in VS Code before it touches your production branch, using built-in editor features, git discipline, and strict test isolation.
Step 1: Commit a clean baseline so every AI change is inspectable
Never accept an AI code edit into a dirty working tree. If an editor extension or an external chat assistant modifies your files while you have uncommitted manual changes, the line between your code and the AI's output blurs. When something breaks, finding the line that caused the regression turns into a guessing game.
Before you run a generation prompt or paste a snippet, stage or commit your current state. Run git status in your integrated VS Code terminal (press Ctrl+ or Cmd+). If your working tree isn't clean, stash your progress with git stash or commit it directly:
git add . git commit -m "baseline before AI refactor"
VS Code shows file changes right away in its Source Control panel (Ctrl+Shift+G or Cmd+Shift+G). With a clean commit baseline, every file the AI modifies appears in the Source Control tree with a distinct status indicator. The gutter in the text editor marks additions in green, modifications in blue, and deletions in red. If the AI produces an unworkable solution, you can discard every change in one click with the Revert action in VS Code and get back to a known, working state.
Step 2: Reproduce the failure before you touch anything
When AI-generated code introduces a bug or fails to fix the original problem, don't type "it didn't work, fix it" into your prompt window. Chaining prompts without diagnosing the failure invites compound hallucinations. The model will invent new code to satisfy your complaint without understanding why the previous attempt broke.
Force a deterministic reproduction first. Create a minimal script, a failing unit test, or an isolated curl command that consistently triggers the error. For example, if the AI wrote a token refresh utility, call that function directly with an expired token payload and watch the runtime exception.
Capture the exact error message, stack trace, and exit code. Watch closely for subtle behavioral bugs. A common failure in generated backend code involves date conversions, such as parsing an expiresAt Unix timestamp in seconds as milliseconds, which makes tokens look expired immediately. If you don't have a concrete input and output that proves the failure, you can't tell whether later fixes solve the problem or just mask it.
Step 3: Review the AI's diff line by line, including imports and dependencies
Treat every AI diff like a pull request from a contributor you've never met. Open the VS Code Source Control view, click each modified file, and read the side-by-side diff line by line. AI models often modify parts of your code you never asked them to touch.
Inspect three high-risk areas during your review:
Imports and package versions. Models often import methods that were deprecated two versions ago or invent helper functions from third-party libraries that don't exist. Check your package.json, requirements.txt, or go.mod against the imported symbols.
Removed edge cases. Asked to fix a specific bug, language models often wipe out existing error-handling blocks, type assertions, or null checks to produce shorter code. Check the red deletions column in VS Code carefully.
Shadowed variables and async control flow. In JavaScript, TypeScript, and Python, AI code often drops await keywords or introduces variable shadowing inside nested closures, which causes silent failures that still compile.
Use VS Code's inline diff view to leave review comments for yourself, or toggle the staging button next to individual hunks. Stage only the hunks you understand and have verified personally.
Step 4: Debug with breakpoints, conditional breakpoints and logpoints in VS Code
Dumping console.log or print statements into AI-generated functions pollutes your git history and clutters the context window if you feed files back to the model later. Use the native VS Code debugger instead.
Open your run configuration in .vscode/launch.json and launch your debug target with F5. Set standard breakpoints by clicking the margin to the left of the line numbers. Step through the execution path with F10 (Step Over) and F11 (Step Into) to see whether the AI's logic follows the path you intended.
In loops or asynchronous queues, standard breakpoints interrupt execution too often. Right-click the gutter and select Add Conditional Breakpoint. Enter an expression such as user.role === 'admin' or items.length === 0 to halt execution only when the problematic state occurs.
If you need runtime context without pausing the thread, select Add Logpoint. Logpoints write dynamic variables to the Debug Console using bracket interpolation (for example: Current status: {order.status}) and leave your source code untouched. You can inspect runtime scope, variable assignments, and the call stack in the VS Code Run and Debug sidebar without writing temporary debugging code that you might accidentally commit.
If you'd rather automate debugging inside the editor, OxCode includes a dedicated Debug task mode. OxCode is an AI software engineer that lives in your editor, working in VS Code, Cursor, Antigravity, and other VS Code-based IDEs. It understands your codebase before it touches it, and it routes prompts to the most efficient model for the job, so you can diagnose execution failures without leaving your development environment.
Step 5: Run the relevant tests in isolation and separate generated tests from executed tests
An AI that writes both application code and unit tests will often write tests that validate its own hallucinations. A model might write an authentication function that returns true on invalid inputs, then write a unit test asserting that invalid inputs should return true. The test suite passes with green checks, but the business logic is broken.
Keep a strict boundary between tests the AI wrote and tests you have actually run and verified. Never run generated tests blindly alongside your core test suite.
First, isolate your execution. Open the VS Code Testing panel or use your terminal to run only the test file related to the change:
npx vitest run tests/auth.test.ts
Second, test the test. Temporarily introduce an intentional syntax error or inversion into your production function to confirm the test fails. If you invert a boolean check and the AI-generated test keeps passing, the test is useless.
Third, run your pre-existing test suite against the AI's changes before adopting new tests. Your legacy suite is your application's actual contract. If the AI changed existing behavior to make its own patch work, your existing integration suite will expose the regression immediately.
Step 6: Verify the fix: rerun the repro, the wider suite, type checks and linters
One passing test doesn't make a fix complete. Before committing the changes, run a four-point verification routine in your VS Code terminal:
Re-run the reproduction step. Execute the exact reproduction script or payload from Step 2. Verify that the previous failure is gone and the output is what you intended.
Run the type checker. AI assistants often bypass strict typing by injecting any casts or ignoring undefined object properties. Run tsc --noEmit or your language's static analysis tool (such as mypy) to confirm zero type regressions.
Run your project linter. Execute ESLint, Ruff, or Biome to catch unused imports, unhandled Promise rejections, and stylistic drift introduced by the model.
Execute your full test suite. Run the entire unit, integration, and end-to-end suite across your project to verify that the change didn't cause side effects elsewhere.
Security matters just as much during verification. AI agents can suggest or run dangerous terminal operations that modify environment variables or touch global files. In tools like OxCode, approval-first commands block unauthorized shell executions, so you explicitly approve every command before it touches your machine. OxCode also includes Security and Plan task modes alongside its project memory, which gives solo developers structural controls when verifying automated patches.
What to do next: install the OxCode extension and make this a habit
A strict verification routine turns AI coding from a chaotic gamble into a reliable productivity boost. Apply these steps to every AI snippet: commit your baseline, reproduce failures deterministically, review diffs, debug with breakpoints, isolate your tests, and verify across your whole suite.
If you want an assistant that runs, tests, and verifies code on its own, install the OxCode extension from the Visual Studio Marketplace. OxCode is designed to plan, write, test and debug code. It runs what it writes in a sandbox and catches and fixes its own bugs before calling a task done. It knows when to act, when to ask and what never to touch.
OxCode resolved 410 of 500 SWE-bench Verified instances on its first attempt, scoring 82%, and solved 73 of 89 Terminal-Bench 2.1 tasks. With over 6,500 downloads, it ranks among the top 4 coding agents globally. Individual developers in India can use the Free plan with 15 runs per day, or upgrade to Starter at ₹899 per month (40 runs per day) or Pro at ₹3,999 per month (150 runs per day). First-time users get Starter at ₹99 and Pro at ₹999 for the first two months on the OxCode pricing page.
Conclusion
Relying on unreviewed AI output creates hidden technical debt that surfaces when clients or users least expect it. Working productively with coding assistants in VS Code comes down to owning verification yourself. Treat every AI suggestion like a pull request from an unverified contributor: inspect the diff, check runtime behavior with real breakpoints, and run tests independently.
If you want an editor-native assistant that plans, runs sandbox tests, and fixes its own mistakes before calling a task complete, install the OxCode extension. Use the 15 daily free runs or the ₹99 two-month Starter intro offer to protect your codebase before you ship.