Cursor Keeps Writing Wrong Code? The Problem May Be Context, Not Prompts

Many developers have experienced this: you ask Cursor to fix one bug, and it conveniently creates three new ones; you remind it to "use the correct quotes," and next time it still gets it wrong. So people start doubting the model's capability, rewriting prompts over and over, changing wording, adding exclamation marks. But an observation from a short YouTube video points to a more fundamental cause: the problem often isn't whether the prompt is well written, but that the context given to the AI is incomplete.

One Bug Becomes Three: The Error Isn't in the Model Itself

The scenario described at the start of the video is something almost everyone has encountered: you ask Cursor to fix a bug, but it introduces three new problems; then you ask it to "pay attention to quotes," and the same error appears again. Intuitively, this feels like the model isn't smart enough, or the prompt isn't precise enough.

But think about it from another angle: if you pulled in a developer who just joined the project and only said "build this feature," without showing them the code structure, without giving error messages, and without saying what the expected result is, they would most likely have to guess too. An implementation built on guesses will naturally run into pitfalls everywhere. AI coding tools face the same situation—the world it sees is entirely determined by the portion of context you provide.

Context Determines Output Quality More Than Prompts Do

The video uses a very apt analogy: a new developer who doesn't understand the project can only guess, and Cursor is the same. It doesn't lack "understanding ability"; what it lacks is the raw material needed for judgment.

According to the video's summary, when giving Cursor a request, besides explaining "what to do," you should also proactively include three types of information:

• Relevant files: let the AI see the code directly related to the task, rather than blindly guessing across the entire repository
• Complete error information: including stack traces and original log text, not just "it errored"
• Expected result: clearly tell it what the correct behavior should look like, so it can align with the goal

These three types of information essentially fill in the "project background" for the AI. The more complete the context, the less room the model has to guess, and the lower the probability of making basic mistakes.

In the context of AI coding tools, "context" refers to all the information the model can see when generating a response, including conversation history, code snippets you paste, open files, error logs, and system prompts. Mainstream tools like Cursor do not automatically read the entire codebase—limited by the model's context window (the maximum number of tokens it can process at once), it usually retrieves only the files relevant to the current problem. This means that if developers do not proactively provide key files, the model can only reason based on incomplete information, producing "hallucinated" patches. Understanding this helps turn complaints that "the model isn't smart" into an engineering habit of "how to feed the context fully."

Plan First, Change in Small Steps, Run Tests Last

Supplementing context alone isn't enough; the operating rhythm is equally critical. The video's suggestion is three steps: first have Cursor provide a plan and confirm the approach is sound; then make small-scope changes to avoid rewriting large chunks of code at once; finally run tests to verify the result.

The value of this process is that it turns "invisible guessing" into "inspectable intermediate artifacts." If the AI's planned direction is wrong, you can correct it before it touches the code, rather than waiting for the error to spread across multiple files and then rolling back. Small-step changes keep the impact of each mistake controllable, and tests are the final line of defense.

"Small-step changes" actually correspond to the idea of incremental development in software engineering, and are also related to the failure modes of AI-generated code: when rewriting large chunks of code at once, the model needs to maintain logical consistency across a long output; any single deviation cascades and amplifies, and once an error occurs it is hard to locate which step introduced it. Breaking tasks into verifiable small chunks, changing only a small amount of code each time and running tests immediately, allows errors to be discovered and corrected as soon as they appear. This approach is akin to test-driven development (TDD)—clarify the expected behavior first, then use tests as a regression safeguard, giving every AI change an objective acceptance criterion.

Better Context Reduces Errors, But Review Is Still the Developer's Job

The video ends with a point that is easy to overlook: better context can reduce errors, but reviewing AI-generated code remains the developer's own responsibility. Context optimization can improve the success rate, but it cannot transfer responsibility to the tool.

This is worth remembering for every team that heavily uses AI coding tools. Preparing context thoroughly, breaking the process into small steps, and running tests—these practices reduce rework costs, not the need for human oversight. AI is responsible for generation; the person judging right from wrong is always you, sitting in front of the screen.

One-line Summary

AI coding errors often stem from incomplete context; supplying full information and verifying in small steps can improve results.

Paragraph Summary

The article addresses a common developer frustration—asking Cursor to fix a bug only to introduce more errors, with repeated reminders failing to help—and offers an explanation different from "change the prompt": the root cause is the incomplete context provided to the AI. Using the analogy of "a new developer who doesn't understand the project can only guess," the author points out that you should proactively include three types of information: relevant files, complete error messages, and expected results, to narrow the model's guessing space. At the operational level, it recommends having the AI produce a plan first, confirming the approach before making small-scope changes, and finally running tests to verify. The article concludes by emphasizing that context optimization can lower error rates, but reviewing generated code is always the developer's own responsibility.