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We are in the middle of a new era of software engineering, where AI coding assistants are no longer just autocomplete helpers but valuable collaborators in the development and debugging process. These tools can speed up the creation of scripts, help navigate unfamiliar languages, and reduce the time spent on repetitive tasks. Yet, the engineer's role remains central: applying expertise, understanding the problem space, and ensuring solutions are accurate, secure, and effective. AI acts as a helping hand that makes the process of creation faster.
In this article, I will share several real-time examples to show how AI assistants are changing development and debugging workflows, from scripting with unfamiliar languages to working with complex APIs and debugging.
Real-Time Example #1: Writing a Python Script Without Being a Python Developer
One of the first times I saw the real power of Cursor AI was when I needed a Python script to get a list of OrgIDs from production that had expired subscriptions for over six months. The goal was to identify inactive organizations so their applications could be offloaded and the overall cost reduced. The challenge was that I rarely write Python.
Instead of spending hours learning the syntax and looking up library usage, I opened Cursor AI and described my goal in plain language. I provided context about the APIs I had available, the data I wanted to pull, and the output format I needed. The cursor returned a working Python script that handled authentication, API calls, and data parsing.
Here's an example of the prompts I used:
The Human Touch
Cursor AI responded with a working Python script that handled authentication, API calls, and data parsing, stitching all of this together into a single runnable flow. The first draft wasn't perfect. Some API parameters were slightly off, and I had to adjust the pagination logic. The format of the expiration date validation was also something that needed to be manually corrected to get the expected results. This is where the human touch mattered.
To validate the output, I did a basic sanity check since these were only GET API calls. I printed a few sample results to confirm that the OrgIDs matched what I expected and that the expiration dates were returned in the correct format. Once I verified the data structure and confirmed that the expired accounts were being identified correctly, the script was ready to use.
Real-Time Example #2: Identifying Hidden Dependencies During a Migration
During a recent migration project, I worked on moving several interconnected services to a new environment. Since each service had its own configuration and deployment setup, the plan was to migrate them one at a time. The challenge was identifying all dependencies so that configurations could be updated correctly for services that had already moved, as well as for those still running in the old environment.
This task required a clear understanding of the service and its caller-callee relationships. While I knew the major dependencies, there were hidden or indirect ones defined in configuration files and environment variables. Instead of manually tracing each dependency across multiple repositories, I turned to Cursor AI for assistance.
Here's an example of the prompts I used:
The Human Touch
Cursor not only flagged known integrations but also pointed me toward configuration paths involving runtime accounts that needed special access. I noticed that the prompts often needed refinement to improve accuracy. For instance, instead of using generic references, I specified the exact repository path so that Cursor could locate the correct dependency definitions. With a few follow-up prompts, I confirmed which of these dependencies were still active and which could be ignored.
I used my understanding of the system to interpret these findings and validate whether each dependency was still relevant for the migration. By combining this AI-driven analysis with domain knowledge, I uncovered subtle dependencies quickly and reduced the risk of post-migration issues.
Real-Time Example #3: Debugging Integration Issues Faster
During one of our service upgrades, I encountered intermittent failures in API calls between two components after deployment. The logs were unclear, and reproducing the issue locally was difficult because the integration involved multiple environments and feature flags.
I used Cursor AI to speed up the investigation. I started by summarizing the issue and pasting a portion of the stack trace, then asked it to identify possible root causes and point out where in the code the behavior might originate.
Here's an example of the prompts I used:
Cursor initially identified a few possible causes, including a version mismatch in an internal SDK that had been updated in one service but not the other. It also suggested checking a specific feature flag that toggled new authentication logic, which turned out to be enabled only for certain orgs. That explained why the issue appeared intermittently across some accounts and not all.
The Human Touch
Cursor couldn't definitively pinpoint the issue at first, but when I followed up by sharing exact log messages from Splunk, it was able to correlate them with specific error patterns and narrow the scope further. Initially, I assumed the problem might be related to database configurations, so I asked Cursor to generate queries to check the network setup, even though I wasn't familiar with the database schema. Cursor analyzed the DB config and curated SQL queries that let me confirm whether affected orgs were tied to the new authentication flag.
By combining these AI-generated insights with targeted log analysis and production context, I was able to confirm that the failures occurred only for orgs where the feature flag was active. Cursor accelerated the investigation, but it was the iterative refinement and validation through real logs that led to the correct root cause.
Benefits and Pitfalls Observed
Benefits
* Speed: Tasks that once took hours can now be prototyped in minutes.
* Accessibility: Engineers can work across unfamiliar languages or frameworks with far less ramp-up time.
* Reduced context switching: AI tools help developers stay focused within their coding environment, minimizing the need to constantly reference documentation.
Pitfalls
* AI hallucinations: Generated code can include incorrect API parameters or logic that looks valid but fails at runtime.
* Security: Every AI output must be validated, especially when handling sensitive tokens, credentials, or data flows.
* Risk of over-reliance: When AI handles too much of the process, engineers may lose touch with core fundamentals and debugging intuition.
Conclusion
Across these real-time examples, it is clear that AI is reshaping how engineers build and debug software. Tools like Cursor are not replacing developers; they are becoming copilots that make problem-solving faster and more focused. The real power comes from collaboration, where AI accelerates the "how," and humans define the "why" and "what."
The key is balance. Let AI handle the repetitive and mechanical parts of coding, but never hand over your judgment. Always verify its outputs (especially before using them in production), question its suggestions, and steer it with context only you understand. AI thrives on direction, and that direction must come from you.
When used wisely, AI becomes a powerful extension of an engineer's toolkit. It makes debugging faster, development smoother, and exploration easier. But it is the human insight, creativity, and critical thinking behind every prompt that turns code into something meaningful.
