Artificial Intelligence (AI) has revolutionized software development, making it faster and more efficient. However, many developers still struggle with AI coding tools, often making mistakes that lead to frustration, inefficiencies, and broken codebases. In this guide, we’ll explore the best practices for AI-assisted coding, focusing on the most common mistakes and how to improve your workflow.
1. No Planning
Jumping straight into coding without a plan is a recipe for disaster. A well-thought-out plan can save hours of debugging and restructuring later.
My Planning Hack
I turn on ChatGPT voice and have a one-on-one conversation about what I want to build. After 15 minutes, I ask:
“Write me a well-structured draft on all the things we’ve finalized in this conversation.”
This way, I use AI as my:
- Brainstorming buddy
- Critique
- Web researcher
- Draft writer
The result? A one-page document outlining the core features of my MVP.
Don’t build blindly. Plan before you hunt.
2. No Knowledge Base for AI Models
AI coding models work best when they have a reference point. Reducing hallucinations and irrelevant code generation requires a structured knowledge base.
How to Build a Knowledge Base for AI Coding Models
After drafting my idea, I create structured documentation:
- Product Requirements Document (PRD) – What the app does
- App Flow Document – How users interact with the app
- Tech Stack Document – The technologies being used
- Frontend Guidelines – UI/UX rules
- Backend Structure – API architecture and database schema
This documentation acts as a reference, ensuring AI models generate relevant and accurate code. Learn more about AI Documentation
3. Not Picking the Right Tools
Every AI coding tool has its own superpower. Choosing the wrong tool can lead to inefficiencies.
AI Coding Tool Comparison:
| Tool | Strengths | Weaknesses |
|---|---|---|
| ChatGPT-4 | General-purpose coding, debugging, writing docs | May hallucinate in complex logic |
| Claude 3.5 | Excellent for writing and structuring code | Not great for debugging |
| Gemini Flash 2.0 | Scanning and refactoring large codebases | Lacks execution speed |
| Cursor/Windsurf | AI-powered IDE for smart code execution | Needs AI-specific setup |
Check out the best AI coding tools
4. Not Picking the Right Tech Stack
AI models are trained on specific programming languages. Using the right tech stack leads to better quality code with fewer errors.
AI-Friendly Tech Stacks
Frontend
- Next.js
- Vite
- Flask (for Python-based projects)
Database
- Supabase (PostgreSQL)
- Firebase
Authentication
- Clerk.dev
- Supabase
- Firebase
AI Models
- OpenAI
- Claude
- Gemini
Best AI-Compatible Programming Languages
| Language | AI Model Effectiveness |
| Python | Excellent |
| JavaScript/TypeScript | Great |
| Rust | Decent |
| Ruby | Limited (but improving) |
5. Not Building Step by Step
Letting AI plan the entire development process often results in messy, unstructured code.
The Fix: My 60-Step Implementation Plan
Instead of letting AI decide everything, I am creating a step-by-step execution plan with 60+ checkpoints to ensure nothing gets skipped. AI should only be used to execute the plan, not to decide what to do next.
6. No Debug Prompting
Debugging AI-generated code can be painful. Use these techniques to improve debugging:
- Attach the error and say: “Use chain of thought reasoning to find the core issue first and then plan step by step to fix the issue.”
- Ask AI to “follow best coding practices. Search the web and find the fix for this issue.”
- Only attach relevant files to improve AI’s focus.
7. Not Using Multiple AI Models
No single AI model can do everything. Use different models for different scenarios:
In Cursor/Windsurf:
- Claude Sonnet 3.5 → Best for writing and executing code
- GPT-4 o1/o3-mini-high → Best for debugging complex errors
- Gemini Flash 2.0 → Best for scanning and updating entire codebases
8. Not Using Starter Kits
Why start from scratch every time? Use boilerplates to speed up development and reduce errors.
I am preparing 4+ pre-built boilerplates specifically designed for AI coding to reduce token usage and avoid unnecessary errors.
9. Quitting Too Early
AI coding is exciting at first, but frustration kicks in when errors start piling up. Expect hundreds of build issues and AI mess-ups.
The Solution:
- Strong Foundation → Build rules and documentation to keep AI coding structured
- Version Control → Use Git to manage source code changes effectively Learn more about Git version control
Final Thoughts
AI-assisted coding is powerful, but only if used correctly. By avoiding these common mistakes and applying structured workflows, you can build faster, smarter, and more efficient applications.
Plan before you code. Structure before you execute. Debug with intelligence. Adapt and improve.