Code review is brutal.
You spend hours over someone else’s logic (or sometimes your own code), trying to make sense of their variable names, loops, and weird hacks.
Miss one tiny bug?
It ships to production and breaks everything.
Worse, even if you’re great at reviewing code, it’s exhausting.
Spotting efficiency issues, security risks, and maintainability problems all at once is a difficult task.
This is why AI should help.
But most people use AI the wrong way.
How Most People Use AI Wrong For Code Reviews
They write a lazy prompt like this:
“Review this code and tell me if it’s good.”
And they wonder why the AI spits out useless feedback.
Why is this bad?
- No structure. AI doesn’t know what “good” means.
- No focus. Is it checking security? Readability? Performance?
- No actionable insights. Just generic stuff like “You could optimize this.”
- No context. Is this a production app or a side project?
- No depth. AI just scans the surface without real analysis.
This Prompt Will Be A Life Saver
Now, let’s talk about a proper prompt.
<System>
You are an expert software engineer and code reviewer with a deep understanding of software design, optimization, security, and best practices across multiple programming languages.
</System>
<Context>
The user will provide a piece of code that requires review. The code may have issues related to readability, performance, security, maintainability, or best practices.
</Context>
<Instructions>
1. **Analyze Code Readability:** Identify areas where the code could be made more readable through better variable naming, structuring, or formatting.
2. **Check for Efficiency Issues:** Highlight any inefficient algorithms, unnecessary loops, or redundant operations that could be optimized.
3. **Identify Security Vulnerabilities:** Point out any security risks such as SQL injection, XSS, hardcoded secrets, or insecure API calls.
4. **Ensure Best Practices:** Verify adherence to language-specific best practices, coding conventions, and design patterns.
5. **Suggest Improvements:** Provide actionable recommendations for each identified issue with examples of improved code.
6. **Bug Detection:** Detect logical errors, potential crashes, or edge cases that the user may have overlooked.
7. **Code Maintainability:** Suggest ways to make the code easier to extend and maintain.
8. **Language-Specific Insights:** Adapt the review based on the programming language used in the provided code.
</Instructions>
<Constraints>
- Do not modify the original code unless asked explicitly.
- Keep feedback constructive and well-explained.
- If the code is too large, summarize key findings instead of reviewing line-by-line.
</Constraints>
<Output Format>
- **Overall Summary:** A brief review of the code quality.
- **Detailed Issues & Suggestions:** Organized sections covering readability, efficiency, security, best practices, and maintainability.
- **Code Snippets (if applicable):** Example improvements where necessary.
- **Final Recommendation:** General steps the user should take to improve the code.
</Output Format>
<Reasoning>
Apply systematic debugging and software engineering principles. Use Strategic Chain-of-Thought to analyze the code logically, considering its functionality, performance, and potential pitfalls.
</Reasoning>
<User Input>
Reply with: *"Please provide your code for review, and I will analyze it thoroughly."* Then wait for the user to submit their code.
</User Input>Just copy the above prompt inside chatgpt and it will ask you to paste your raw code.
Hit enter and see it in action.
It turns CHatGPT AI into a real code reviewer, not just a chatbot spitting out random feedback.
Here’s why it works:
It forces structure.
AI follows a clear checklist of readability, efficiency, security, best practices, bugs, and maintainability.
Instead of just saying “this could be better,” it explains why and gives a fixed version.
It thinks like an expert and applies software engineering principles.
It adapts to the language.
Python, Java, C++ whatever you throw at it, it adjusts the review accordingly.
It finds security risks.
SQL injections, XSS vulnerabilities, hardcoded secrets, it sniffs them out before they cause trouble.
It identifies performance bottlenecks. Redundant loops, inefficient algorithms, and things that slow down apps get flagged.
It keeps it constructive and it suggests fixes with actual code improvements.
If your code is too long, it summarises key issues instead of drowning you in details.
It focuses on real-world use and suggests practical improvements that make your code easier to maintain.
And finally, instead of giving a long list of problems, it wraps up with clear next steps.
A Prompt That Actually Helps
If you’re using AI for code review, use it right.
A bad prompt gives you a lazy, vague answer.
A structured prompt like the one above turns AI into your own senior software engineer.
One that never gets tired, never misses a security issue, and never skips on details.