What to do if bugs always appear when writing AI code, 6 troubleshooting methods to make AI programming more reliable in 2026

What should I do if there are always bugs when writing AI code? 6 troubleshooting methods to make AI programming more reliable in 2026

Writing code using ChatGPT, Claude, Cursor, and Copilot has become a daily routine for developers, but many people have encountered the same embarrassment: the code given by AI looks very similar at first glance, but when it runs, it either reports an error or has incorrect logic. After a long time of modification, it is better to write it yourself. The problem is often not the AI ​​itself, but the way it is used. This article gives 6 effective troubleshooting methods that have been tested in 2026, switching from prompt words to models to review processes to help you reduce the error rate of AI programming to an acceptable range.

Why does code written by AI have bugs?

To solve a problem, you must first understand the problem. There are generally several types of sources of bugs in AI-generated code.

The first category is incomplete context. AI does not know your project structure, dependency versions, and existing function naming conventions. It relies on its own training data to make assumptions, which will naturally easily conflict with your actual environment.

The second category is model illusion. AI will make up non-existent APIs, non-existent library functions, and non-existent syntactic sugar. This is an inherent problem of large language models, and even the latest flagship models cannot completely avoid it.

The third category is that the task complexity is too high. Let AI write a complete functional module at a time, and the hidden logical branches in the middle can easily be missed. The code that comes out may seem complete, but it will break down under certain boundary conditions when running.

The fourth category is version mismatch. AI training data has an expiration date. The latest version of the framework you are using may have never been seen before, and the generated code uses an outdated API.

Once you understand these four root causes, the subsequent troubleshooting methods will be more targeted.

The first trick: feed the whole context

The most effective troubleshooting method is to block it from the source. Every time before letting AI write code, the relevant context must be clearly fed. This includes: what language, framework, and version your project uses, what your directory structure is like, what related functions and classes you already have, and what your code specifications are (such as indentation, naming style, error handling mode).

In integrated tools like Cursor or Claude Code, you can directly drag files or use @ to reference files, and AI will automatically read the context. In pure web conversations such as ChatGPT, you need to manually paste the relevant files in, or use the project file upload function.

A practical tip is to create a README.dev file in the project root directory, which clearly states what technology stack the project uses, what agreements there are, and the responsibilities of key modules. Paste this file every time you open a new conversation as the first message, and the code generated by AI will significantly fit your project style.

The second trick: let AI make plans first and then write code

If you directly write an X function, AI will dive in and start coding. The result is often that the details are not in place or deviate from the requirements. A more stable approach is to do it in two steps.

The first step is to let AI give you an implementation plan. When writing the prompt, please do not write the code first. Use a structured list to tell me how you plan to implement this function, including how many functions it will be broken into, what each function does, which libraries it depends on, and what boundary conditions need to be handled.

After seeing the plan, you can quickly judge whether the AI's idea is correct. If it's not right, it's much less expensive to adjust it in the planning stage than to rework it in the coding stage. If it is correct, let it write the code as planned.

This workflow of planning first and then coding is also emphasized in the Claude Code usage guide recommended by Anthropic. The actual development efficiency will be much higher than directly coding.

Tip No. 3: Iterate in small steps instead of writing one big paragraph at a time

The probability of bugs in large sections of code increases exponentially. If you write 300 lines of functions at a time, the probability of AI errors is much higher than if you write 30 lines, and bugs are difficult to locate.

A better approach is to break the task into small steps, and only ask the AI ​​to write a function or a small module for each step, and then continue to the next step after it runs through. For example, to create a user management function, the first step is to write the data model and run the test and pass; the second step is to write the registration interface and run the test to pass; the third step is to write the login interface, and so on.

This small-step iteration model not only has a low bug rate, but also allows you to git commit each step, so you can roll back at any time if something goes wrong. Cursor's Composer and Claude Code's Plan mode both encourage this approach.

The fourth trick: run tests and lint in time

Do not deploy or integrate the code immediately after writing it. Run unit tests and static checks first. These two steps can block at least half of low-level bugs.

If the project itself does not have complete test coverage, let AI write unit tests for one or two core scenarios, and then repair them after running them to see which ones fail. It is easier to get it right when writing tests for AI than when writing functional code, because the logic of the test is relatively simple and clear.

Static analysis tools such as ESLint, Pylint, and TypeScript compilers can find a large number of low-level errors without running the code, such as undefined variables, type mismatches, unused imports, etc. Code generated by AI often introduces small problems like this, which can be fixed in a few seconds by running lint.

Integration testing and end-to-end testing are a more stringent layer of defense, suitable for key modules or regression testing before going online.

Step 5: Try changing the model

The performance of different models on different tasks varies greatly. If the code written by ChatGPT repeatedly fails, try using Claude. If Claude doesn't work, try Gemini instead. If the flagship model doesn't work, try using code-focused tools such as Cursor, Aider, and Cline.

There are several recognized directions in the industry. Claude has a good reputation for long context understanding, rigorous reasoning, and coding style, and is suitable for complex refactoring and architecture design. ChatGPT's GPT-4o performs stably in rapid generation, multi-modality, and tool calling, and is suitable for use as an interactive programming assistant. Gemini has advantages in ultra-long contexts (millions of token levels) and is suitable for throwing into the entire code base for global analysis.

In terms of domestic models, DeepSeek, Kimi, and Zhipu are becoming more and more mature in coding tasks, and their prices are more friendly, making them suitable as low-cost alternatives for daily development.

Don't be loyal to any one model, use whichever one works best for your current task. Opening two or three AI dialogue windows at the same time to check the output is a common practice for many senior developers in 2026.

The sixth trick: manual review, don’t believe everything

The last and most important trick: any AI-generated code must be manually reviewed before being incorporated. The AI's output looks very confident, but you can't really believe it just because it says this code can run.

The review focuses on several aspects. The first is to check whether the API call really exists and whether there is a fabricated function signature. The second is to check whether error handling covers critical failure paths. The third is to check the logic related to concurrency, status, and side effects. This is the area where AI is most prone to problems. The fourth is to check whether the code style, naming, and comments comply with your project specifications.

You don’t have to stare at the review line by line. You can let AI help you do the first self-review. The prompt is written as "Please review the code you just generated to find possible bugs, security risks, and performance issues." This step often allows AI to discover its own mistakes, which is more efficient than pure manual work.

Common bug patterns and quick troubleshooting checklist

If you encounter AI code bugs at work, you can quickly go through the following list.

Module import error: Check whether the package name is correct, whether the version is compatible, and whether the init file is missing. AI often makes up libraries that don’t exist or uses outdated APIs.

Type error: The type hint of TypeScript or Python does not match. Usually the AI ​​misunderstands the interface signature and pastes the source code of the target function to it for regeneration.

Logic error: The output does not match expectations, usually because the boundary condition processing is incomplete. Add a few prints or breakpoints to locate the specific step where the problem occurs.

Performance issues: Expensive operations, database N+1 queries, and memory explosion are scheduled in the loop. These are areas that AI is not good at and require manual optimization.

Security vulnerabilities: SQL injection, XSS, authentication bypass, sensitive information leakage, AI often leaves holes in these places, and key scenarios must be reviewed manually.

Concurrency issues: race conditions, deadlocks, inconsistent states, AI is weak in concurrent reasoning, and it is best not to give this part of the code full power to AI.

Workflow with code review tools

If your team is using GitHub or GitLab, you can use code review tools to improve efficiency. GitHub Copilot's PR review, CodeRabbit, Greptile and other tools can automatically review pull requests and pick out potential bugs and style issues.

At the IDE level, Cursor, Claude Code, and Aider all support feeding git diff to AI for review, and then automatically change the code based on the review comments. With this combination, even if the first draft generated by AI has bugs, after automatic review and iteration, the quality of the code that is finally merged into the main branch can be maintained at an acceptable level.

However, tools are only auxiliary, and manual review cannot be saved. The cost of bugs will be magnified many times in a production environment. It is much more cost-effective to spend an extra ten minutes reviewing in the early stage than to fix bugs all night later.

FAQ

Can AI code writing completely replace programmers?

Not in the short term. AI is indeed very efficient in repetitive coding, template application, document review, and unit test generation, but human engineers are still irreplaceable in matters such as system architecture, requirement understanding, cross-team communication, production environment debugging, and security and compliance judgments. Treating AI as a powerful assistant and maintaining your own judgment and control over the entire system is currently the most robust approach.

Which model is best for writing code

There is no standard answer. For simple scripts and prototypes, use ChatGPT’s GPT-4o or the free DeepSeek. Complex refactoring and long-context projects are best experienced with the Claude series. Cursor, Copilot, and Claude Code are used for daily IDE integration. If the budget is tight, you can use DeepSeek or locally deployed Qwen or Llama. It is recommended to prepare two or three at the same time and switch according to the task.

Will writing code with AI reveal company secrets?

There will be risks. The commercial versions of ChatGPT Plus and Claude Pro usually promise not to use your input to train the model, but the transmission and storage processes are still in the cloud. If it is very sensitive code, it is recommended to use a locally deployed open source model (Ollama runs Qwen, Llama, DeepSeek) or a company-built private deployment. It is perfectly possible to use cloud AI for daily non-sensitive code.

How to choose between Cursor, Copilot and Claude Code

Cursor is an independent editor with the most complete experience and is suitable for people who are willing to change IDEs. Copilot is a VS Code plus JetBrains plug-in, suitable for existing users who don’t want to change IDEs. Claude Code is a command line tool suitable for developers who like terminal workflow. The underlying AI capabilities of all three are good, it mainly depends on your work habits. You can install them all and try them out for a week before deciding which one to use.

How do I let AI learn my project specifications?

The most effective way is to create a specification file in the project root directory, such as .cursorrules, CLAUDE.md, .github/copilot-instructions.md, in which the naming rules, error handling mode, annotation style, and technology stack selection are clearly written. Cursor and Claude Code will automatically read these files, and Copilot can also read them through plug-in configuration. AI already knows your specifications before each conversation, and the generated code will be more consistent.