In the rapidly evolving landscape of AI-assisted development, how we communicate with our AI tools can make the difference between frustration and success. My recent experience with Cursor AI highlighted the stark contrast between throwing everything at an AI at once versus taking a more methodical approach.
The Overwhelm Scenario
Last week, I found myself staring at a complex project that needed quick implementation. In my haste, I dumped everything into Cursor AI: multiple context documents, a detailed technical guide, and a brief with various requirements and edge cases. My prompt essentially said, “Here’s everything – now build this for me.”
The result? Total confusion!! Cursor attempted to process all the information simultaneously but struggled to prioritise what mattered most. The code it generated mixed concepts from different sections of my documentation, missed key requirements, and created solutions to problems I wasn’t even asking about.
What I got was a complete meltdown, timeouts and a folder with half the working in it that wasn’t of any use what so ever, this isn’t something I haven’t experienced before and I’m sure I speak for most developers this is overwhelming majority of first time experiences with AI especially when it comes to code assistance.
The Step-by-Step Approach
After that frustrating experience, I tried a different approach with a similarly complex project. I provided the same materials – context documents, technical guide, and brief – but this time I structured my interaction differently.
I broke down the project into discrete steps and explicitly prompted Cursor to tackle just one step at a time. This iterative, chain-of-thought approach transformed my experience completely, as my screenshot demonstrates.
Real-World Example: WordPress Block Development
In one telling example, I needed to create a dynamic search filter block for houses in WordPress. Rather than asking Cursor AI to generate the entire solution at once, I started with a simple prompt: “Let’s scaffold the dynamic search filter block for houses.”
The AI first analysed my workspace structure, identifying my custom theme “katomswold” and suggesting creating the block within the theme directory. However, this wasn’t my intended approach.
Instead of accepting this initial suggestion, I questioned the decision: “Is there a reason for setting this up in the theme rather than a standalone plugin?”
This simple question triggered a thoughtful analysis from the AI, which provided pros and cons of both approaches:
Theme Integration Pros:
- Tightly coupled with theme design/styling
- Easier maintenance if specific to this theme
- Simpler deployment for theme-specific blocks
Plugin Approach Pros:
- Portability across themes
- Reusability across sites
- Better separation of concerns
- Survival through theme changes
- Independent distribution
- Better version control
The AI then concluded based on WordPress best practices that for a dynamic search filter that could be useful across different themes, a plugin approach would indeed be better – exactly what I had been thinking.
The Results Speak for Themselves
This interaction demonstrates the power of iterative prompting combined with chain-of-thought reasoning. By breaking down the task and engaging in a dialogue with the AI, I received:
- More thoughtful analysis of architectural decisions
- Solutions aligned with industry best practices
- Code that met my actual needs rather than assumptions
- A learning opportunity as the AI explained its reasoning
Most importantly, the final output was significantly more useful than what I would have received from dumping all requirements at once. The AI wasn’t just coding blindly – it was collaborating with me as a development partner.
Takeaway: Prompt Smarter, Not Harder
The difference between overwhelming an AI and guiding it through iterative prompting isn’t just about getting better code – it’s about creating a more effective workflow. By combining chain-of-thought prompting (asking the AI to reason through decisions) with multi-turn interaction (reviewing and refining at each step), you transform AI tools from basic code generators to collaborative development partners.
Next time you’re working with AI coding assistants, remember: less can be more, and a conversation beats a data dump every time.
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