AI coding assistants are having a moment, and Cursor might be the loudest voice in the room. After two months running it inside a production repo, I’ve lived through the honeymoon, the heartbreak, and—thanks to a laminated prompt sheet—the happy reconciliation. If you’re considering an AI pair programmer or wondering how to reduce tech debt with AI, here’s the unfiltered story.
Why We “Hired” Cursor in the First Place
- Velocity: Boilerplate and CRUD endpoints are developer broccoli. Cursor promised to cook them faster than I could finish a stand-up.
- Coverage: It can generate unit tests while I’m still naming variables.
- Focus: Less time on repetitive tasks means more time on product logic—at least in theory.
Those benefits remain real, but the path was bumpier than the demo video.
Act 1: The Honeymoon
Week 1 was pure bliss. I’d ask for a TypeScript service layer; Cursor delivered an entire file hierarchy with clean interfaces and reasonable naming conventions. Our developer productivity spiked, commits flowed, and we toasted to the future of work.
Act 2: When Things Went Sideways
By Week 3, the cracks appeared:
- Code Vanishing Acts
Cursor “refactored” an auth check straight out of existence. Production noticed. - Folder Explosion
New top-level directories sprouted like mushrooms after rain—apparently confusing modules with a botanical garden. - Strange Commands in CI
Yes, it once triedbrew install cowsayin the pipeline. Our servers prefer minimalism.
Debugging became whack-a-mole. We needed guardrails or a new heart medication.
Act 3: Posting the Rulebook on the Fridge
The fix wasn’t magic; it was prompt engineering plus old-fashioned documentation. Here’s the exact framework that now precedes every Cursor session:
| Rule | Why It Matters |
|---|---|
Docker for builds, pnpm for installs | Avoids rogue OS-level installs and version drift. |
| Analyze the codebase first | Forces Cursor to reuse existing patterns instead of reinventing wheels—and triangles. |
| Ask before destructive actions | No table drops or config nukes without human approval. |
| Reuse > Rewrite | Aligns with our architecture, keeps commits slim, and cuts merge conflicts. |
| Pitch big changes | Schema or queue tweaks require options, rationale, and explicit sign-off. |
| Simplest viable fix | Keeps the AI from over-engineering and inflating tech debt. |
| Debug like a detective | Isolate, plan, then code—exactly what you’d expect from a senior engineer. |
We literally pasted these rules into a top-of-file comment and as the first chunk of every interaction.
Act 4: The Results
- 40 % fewer revert commits (internal Git stats).
- Mean Time to Debug dropped because problems are isolated and explained before code changes.
- Documentation discipline rose; clear READMEs make the AI—and human teammates—smarter.
- Developer morale improved. Nothing torpedoes a sprint like chasing phantom bugs introduced by a “helper.”
Best Practices You Can Steal
- Treat Prompts Like CI Config: A small tweak can break (or save) your build. Version-control them.
- Invest in Documentation: AI coding assistants are only as good as the context you feed them.
- Enforce a Comment-First Rule: Cursor must propose a diff and explain why before touching real code.
- Track Metrics: If the AI isn’t moving velocity or code quality in the right direction, refine the guardrails.
Final Thoughts
AI tools are neither silver bullets nor existential threats—they’re junior devs with endless energy and zero context. Give them context and they become force multipliers; starve them and they’ll happily delete your production secrets.
If you have your own AI debugging tips or Cursor war stories, drop them in the comments or ping me directly. Let’s keep the conversation—and our repos—healthy.
Grab the Prompt
I’ve bundled my Cursor prompt—the very set of rules that keeps our AI on the straight and narrow. Copy it here:
# Terminal commands
Always run `docker` commands in the terminal to build the app and database. To install packages always use `pnpm`.
# Context and Understanding:
Analyze the current code base and understand the project goals, architecture, and existing patterns.
# Avoid Destructive Actions:
Unless otherwise instructed do not perform destructive actions that could harm the development environment or configurations to the application. If absolutely necessary, then ask the user if they would like to proceed with a suggested change before taking action.
# Code Generation:
Before writing any new code, thoroughly check for existing patterns, functions, and modules that can be reused or adapted. Ensure the proposed code aligns with the overall architecture and coding standards of the project.
# Major Architectural Decisions:
When encountering major architectural decisions (e.g., database model modifications, schema changes), present options and rationale. Seek user permission or guidance before implementing changes that could impact the system's structure or performance.
# Pitfall Avoidance:
Be aware of common blindspots such as incorrect pattern implementations or misplaced folder structures. Prioritize simplicity and clarity in solutions rather than over engineering, considering the holistic view of the codebase.
# Optimal Solution Evaluation:
Encourage stepping back to review the entire codebase and considering alternative approaches. Evaluate and propose the most optimal solution based on performance, scalability, and maintainability.
# User Interaction:
Communicate clearly and transparently about proposed changes, ensuring the user understands the implications. Provide options for user feedback and adjustments based on their preferences or specific project requirements.
# When fixing code / debugging
- Please propose the simplest viable approach to solve this problem while minimizing tech debt.
- Refactor only what is strictly necessary—avoid adding new frameworks, libraries, or complex patterns unless essential.
- Summarize your recommendation as a concise, step‑by‑step outline (no code).
- Highlight trade‑offs and any risks in one short paragraph.
- When debugging always load the terminal and the MCP browser tools in the chat and run the debuggermode.
- Try to isolate the problems before writing any code and always create a debugging plan and present it to the user before writing any code.
- Do not create technical debt.