A prompt word makes the Cursor jump back and forth
When using Cursor debugging, how can you efficiently locate the root cause of the problem? The structured prompts provided in this article can help you think systematically and check one by one.
Core content:
1. The composition and application of magic prompts
2. The integration of structured reasoning, iterative problem solving and verification methods
3. Specific steps analysis: identifying the source of the error, screening possibilities, and verifying assumptions
Using Debug in Cursor
Having trouble debugging in Cursor? Try this amazing tip.
Prompt:
Think of 5-7 possible sources of the problem and prioritize them based on likelihood, impact on functionality, and frequency of occurrence in similar issues. Only consider sources that match the error log, recent code changes, and system constraints. Ignore external dependencies unless the log explicitly points to them.
Once you’ve narrowed it down to the 1-2 most likely sources, cross-validate them with historical error logs, relevant system states, and expected behavior. If you find inconsistencies, adjust your assumptions.
When adding logs, make sure they are strategically placed to confirm or rule out multiple potential causes simultaneously. If the logs do not support your hypothesis, come up with alternative debugging strategies before continuing with the analysis.
Before implementing a fix, summarize the problem symptoms, verified hypotheses, and expected log output to confirm that the problem is actually fixed.
This ensures that you do sufficient diagnosis before fixing the code, and don't jump directly to a solution.
Why is this tip so effective?
This prompt combines structured reasoning, iterative problem solving, and verification methods, and conforms to multiple effective prompting patterns in the Pattern Language framework. The following is a detailed analysis:
Contextual Anchoring
Declare goals & set constraints The task is clearly defined as: “Think about possible causes → Identify the most likely source → Verify assumptions through logs → Then make fixes.” This prevents AI from jumping directly to code implementation, but instead ensures that the problem is correctly identified first.
Organized Thought
Hierarchical Decomposition (Task Decomposition) The task is broken down into three clear steps: 1. Identify multiple possible sources of error (exploratory reasoning). 2. Filter out the most likely causes (prioritization & refinement). 3. Verify assumptions through logs (verification phase). This approach can reduce the complexity of debugging and avoid making wrong assumptions at the beginning.
Sequential Guidance prompts the AI to proceed step by step, ensuring that: • No assumptions are made about the root cause of the problem. • Assumptions are verified before code fixes are made.
Incremental Clarity
Layered Prompting: The prompt requires the AI to first list broadly (5-7 possible causes), then narrow down the scope (1-2 most likely sources), and finally verify. This method ensures that the AI gradually converges on the problem instead of giving a single answer at once.
Iterative Correction requires adding logging before fixing the code, which is equivalent to building in a self-checking mechanism to prevent the AI from jumping directly to incorrect solutions.
Maintain logical consistency (Consistency & Coherence)
Enforce Logical Continuity requires that code cannot be modified directly before the most likely cause is found, so that AI does not: • Make arbitrary assumptions too early. • Deviate from the logical chain from problem identification to repair.
Verification & Robustness “Add logs to verify your assumptions” forms a feedback loop to ensure: • Any repair behavior is supported by data. • The debugging process is based on real evidence rather than subjective speculation.
Meta-Thinking & Self-Reflection
Self-Correction Prompts require AI to evaluate assumptions before taking action, avoiding wasted debugging time due to misjudgments.
Confidence Appraisal Through log verification, AI needs to confirm whether its assumptions are reliable before continuing to repair, thereby enhancing the accuracy of problem location.
Summary: What is so powerful about this prompt?
How can I make this prompt stronger?
If you want to make the debugging process more rigorous, you can optimize it further:
Add clear prioritization criteria (e.g. filter the most likely sources of errors based on how often they appear in the logs). Add a post-verification summary step (before fixing the code, summarize what the log confirmed).
This is already a pretty powerful tip, but you can improve it even more.
Optimized English version:
Reflect on 5-7 different possible sources of the problem, prioritizing them based on likelihood, impact on functionality, and frequency in similar issues. Only consider sources that align with the error logs, recent code changes, and system constraints. Ignore external dependencies unless logs indicate otherwise.
Once you 've narrowed it down to 1-2 most likely sources, cross-check them against previous error logs, relevant system state, and expected behaviors. If inconsistencies arise, refine your hypothesis.
When adding logs, ensure they are strategically placed to confirm or eliminate multiple causes. If the logs do not support your assumptions, suggest an alternative debugging strategy before proceeding.
Before a fix, summarize the issue, implement validated assumptions, and expected log outputs that would confirm the problem is solved.
The earliest Chinese and English versions:
English version:
Reflect on 5-7 different possible sources of the problem, distill those down to 1-2 most likely sources, and then add logs to validate your assumptions before we move onto implementing the actual code fix
Chinese version:
Think of 5-7 possible reasons for the problem, narrow them down to 1-2 most likely sources, then add logging to verify your hypothesis before moving on to implementing the code fix
