8 Ways to Improve Your AI Coding Results

Artificial Intelligence (AI) coding tools like GitHub Copilot, ChatGPT, and Amazon CodeWhisperer are transforming how developers write software. These tools can boost productivity, reduce errors, and speed up development cycles; but only if used effectively. Code that is glitchy, ineffective, or even unsafe can result from poorly designed prompts, a lack of context, and an excessive reliance on AI.

 

To help you get the best results from AI coding assistants, here are eight proven strategies to enhance your AI-generated code.  

1. Write Clear and Specific Prompts  

The input that AI models receive is crucial. Vague prompts like *"Write a function to sort data"* may produce generic or incorrect code. Instead, provide detailed instructions:  

✅ Better Prompt:  

"Create a Python function that uses merge sort to arrange a list of dictionaries in descending order by the 'price' key. Add error-handling for values that are not numbers."

📌 Why It Works:  

• Specifies language (Python)  
• describes a data structure (dictionary list).
• Requests a specific algorithm (merge sort)  
• incorporates error-handling 

🔗 Source: [GitHub Copilot Best Practices](https://docs.github.com/en/copilot/quickstart) 

2. Provide Context and Examples 

AI performs better when given contextual information. If you’re working on a specific project, share:  

• Relevant bits of code 
• API documentation  
• Expected formats for input and output 

✅ Example:  

"Here’s my current Flask API route. Modify it to add JWT authentication using PyJWT. Expected response format: { 'token': 'xyz', 'user_id': 123 }." 

📌 Why It Works:  

• Helps AI understand the project structure  
• Minimizes erroneous presumptions  

🔗 Source: [OpenAI’s Guide to Better Prompts](https://platform.openai.com/docs/guides/prompt-engineering)  

3. Break Complex Tasks into Smaller Steps 

Instead of asking AI to "Build a full-stack e-commerce app," break it into smaller requests:  

1. "Create a product listing page in React using filters."
2. Compose a Node. checkout js API endpoint.
3. "Create a PostgreSQL schema for user orders."  

📌 Why It Works:  

• Reduces hallucinations (AI making up logic)  
• Simpler to Debug and Improve

🔗 Source: [Google’s AI Coding Tips](https://ai.google/build/machine-learning/)  

4. Review and Refine AI-Generated Code

Never blindly accept AI code. Always:  
✔ Test for edge cases  
✔ Check for security vulnerabilities (e.g., SQL injection)  
✔ Optimize for performance  

🚨 Common AI Coding Mistakes:  

• Inefficient loops  
• Input Validation is Missing. 
• Hardcoded secrets  

🔗 Source: [OWASP AI Security Guidelines](https://owasp.org/www-project-ai-security/)  

5. Fine-Tune AI with Your Coding Style  

Many AI tools (like GitHub Copilot) learn from your codebase. To improve suggestions:  

• Train it on your past projects  
• Correct its mistakes (thumbs up/down in Copilot)  
• Use comments to guide style preferences  

✅ Example:  

"Follow our team’s React practices: functional components, TypeScript, and Tailwind CSS." 

📌 Why It Works:  

• Maintains a Consistent code Style
• Cuts down on Manual Refactoring

🔗 Source: [GitHub Copilot for Teams](https://github.com/features/copilot/enterprise)  

6. Combine Multiple AI Tools 

No single AI tool is perfect. Use:  

• ChatGPT for brainstorming & pseudocode  
• Copilot for real-time autocompletion  
• CodeWhisperer for AWS-specific code  
• Tabnine for offline suggestions  

📌 Why It Works:  

• Utilizes the advantages of various models
• Lessens reliance on a single tool

🔗 Source: [Stack Overflow Developer Survey 2024](https://survey.stackoverflow.co/2024/)  

7. Stay Updated on AI Coding Trends  

AI evolves rapidly. Follow:  
🔹 New model releases (e.g., GPT-5, Claude 4)  
🔹 Security patches (e.g., AI-generated malware risks)  
🔹 Best practices (e.g., ethical AI coding)  

📌 Why It Works:  

• Stays away from antiquated methods
• Keeps you competitive when applying for jobs.

🔗 Source: [MIT’s AI Research Updates](https://www.csail.mit.edu/research/artificial-intelligence)  

8. Balance AI and Human Expertise  

AI is a tool, not a replacement. The best developers:  
✔ Use AI for repetitive tasks  
✔ Apply critical thinking to complex problems
✔ Maintain deep coding fundamentals

🚀 Future-Proof Your Skills:  

• Acquire knowledge of prompt engineering
• Master debugging AI code  
• Recognize the limitations of AI

🔗 Source: [Harvard’s AI in Software Development Course](https://cs50.ai/)

  

Conclusion: Maximize AI, Minimize Risks 

AI coding assistants can 10x your productivity if used wisely. By following these eight strategies, you’ll generate cleaner, safer, and more efficient code while avoiding common pitfalls.  

💡 Key Takeaways: 

1️⃣ Be specific in prompts  
2️⃣ Provide context  
3️⃣ Break tasks into steps  
4️⃣ Always review AI code  
5️⃣ Train AI on your style  
6️⃣ Use multiple tools  
7️⃣ Stay updated  
8️⃣ Balance AI with human skills  

The future of coding is human-AI collaboration. Master these techniques, and you’ll stay ahead in the AI-powered development era.  

References

1. GitHub. (2024). *GitHub Copilot Documentation*. [Link](https://docs.github.com/en/copilot)  
2. OpenAI. (2024). *Prompt Engineering Guide*. [Link](https://platform.openai.com/docs/guides/prompt-engineering)  
3. OWASP. (2024). *AI Security Best Practices*. [Link](https://owasp.org/www-project-ai-security/)  
4. Stack Overflow. (2024). *Developer Survey*. [Link](https://survey.stackoverflow.co/2024/)