Introduction
Hello, my fellow developers!
I enjoy building quick prototypes with AI chatbots, such as Google Gemini, Claude AI, or ChatGPT. I find them very useful for various tasks, but I’d like to share my honest experience after months of using these tools - both the fantastic aspects and the frustrating ones.
You know me - I do not believe in magic solutions. These AI tools are powerful assistants, but you still need to understand what you are building. However, when used wisely, they can genuinely transform your coding workflow and learning experience.
Google Gemini
Google Gemini has a very generous free plan, and I love it for creating my JavaScript functions. The quality of the output is simply fantastic!
I think that Gemini is an excellent tool for quickly drafting Ajax functions, and this chatbot helped me learn JavaScript in no time. Let me tell you why I chose Gemini as my go-to for frontend development.
When I was working on a recent project that required complex Ajax interactions, I simply described what I needed:
Create an Ajax function that fetches user data from a REST API, handles loading states, and updates the DOM with error handling.
Gemini’s Flash 2.5 output was well-written and with good comments. I have also noticed that Gemini likes the Tailwind CSS Framework. However, you can request it using your preferred framework or remove the class references. It recently helped me to create my own tiny CSS framework for my prototypes. It was lightweight and had only the classes I needed.
Gemini’s API capabilities (as detailed in the official Gemini API documentation) make it particularly strong for web development tasks, and it generated clean, working code that I only needed to tweak slightly.
What impressed me most about Gemini is its understanding of modern JavaScript patterns. It consistently suggests ES6+ syntax, proper async/await usage, and even includes error boundaries. The integration possibilities are extensive, and you can experiment with different Generative AI models using Google AI Studio.
Next, I have asked Gemini Flask 2.5:
Create a fetch function that handles rate limiting and retries with exponential backoff.
It generated:
This is production-ready code with minimal modifications needed!
Gemini’s Debugging Superpower:
Gemini loves debugging indeed. Copy and paste the code, and it will identify issues you may have missed. I had a tricky closure problem in JavaScript that took me hours to spot; Gemini identified it in seconds and explained why the scope was incorrect. This debugging capability has saved me countless hours.
ChatGPT
ChatGPT is also great at drafting Python code; however, in my opinion, it often lacks thoroughness. It provides suggestions on how to accomplish tasks rather than completing the provided task. ChatGPT tends to give you conceptual guidance rather than complete implementations. It is pretty good for learning.
For instance, I was working on drafting a quick CSS Framework (I did not want to be dependent on Tailwind or Bulma, which are both great, by the way). I just wanted to implement several classes to make my little web app with the smallest CSS possible :) ChatGPT was lazy; Gemini did a great job.
When I asked ChatGPT (o4 mini-high) to polish my CSS file, it missed many class and name definitions!
But Gemini? It worked well in going through all my classes, creating a well-structured CSS file as a like it:
The goal here is a truly clean, maintainable stylesheet. I'll maintain all the logic, variables, and responsiveness we've built, while focusing on: Logical Grouping: Re-ordering and consolidating related CSS rules. Comments: Clear and concise comments for each section and important rules. Tabs Improvement: Enhancing the visual design of the tabs for both light and dark modes, and ensuring smooth transitions. Consistency: Double-checking variable usage for borders, shadows, and colours across components. Readability: Overall formatting for easier scanning and understanding. Here's your re-organised and improved CSS file:
Funnily, it messed up with navbar-burger, but it was easily fixed.
ChatGPT’s Limitations in Practice:
ChatGPT struggles with navigating large codebases and understanding the big picture of complex projects. I noticed this when asking it to refactor a multi-file Python project - it would focus on individual functions without considering the broader architecture.
Research indicates that the quality of AI-generated code varies significantly depending on the task’s complexity. This is why I constantly review its suggestions carefully, especially for production code.
Where ChatGPT Shines:
Despite these limitations, ChatGPT excels at:
- Explaining complex programming concepts
- Code documentation and commenting
- Algorithm explanations with step-by-step breakdowns
- Quick syntax reference for unfamiliar languages
Claude AI
The Claude AI is very special. Additionally, you must pay to use it; it offers fewer free tokens than Gemini. However, I find all these tools to be a fantastic addition to my toolbox for down-to-earth prototyping.
What makes Claude special is its thoughtful approach to code generation - it’s designed to be highly performant, intelligent, and trustworthy. Where ChatGPT may provide a quick solution, Gemini excels at specific tasks, and Claude offers comprehensive, well-structured solutions with proper error handling and documentation. The comprehensive Claude documentation explains how to get started.
Claude’s Thoughtful Code Generation:
When I asked Claude Sonnet 4 to create a Python class for handling API requests, it didn’t just give me a basic implementation. It provided:
Notice the type hints, proper error handling, logging, and clean architecture? This is why Claude is quite suitable for serious development work.
Gemini Flash 2.5 also provided an excellent and well-documented code. In the end, Gemini asked me if I would like to add more features, “such as authentication, retry mechanisms, or asynchronous requests.” It is overall an excellent approach for creating prototypes and slowly improving our code.
ChatGPT 4o created a minimalistic, however functional code with the usage examples. It was not well-connected. After providing the basic code output, ChatGPT asked me: “Let me know if you’d like features like retry logic, logging, or async support.”
Surely, ChatGPT 4o knows about what we need; however, it takes a “lazy” approach and gives a “bare minimum.” You will need to provide a detailed prompt to achieve a sound output.
The MCP Revolution
Overall, I love Gemini for all my coding needs. Gemini is an experienced and friendly coding pal. I currently have a paid subscription to ChatGPT, which is excellent for learning and content generation; however, I am considering moving to Claude Desktop. Yes, they have this novelty toolset that comes from the MCP for easier AI integration.
The Model Context Protocol (MCP) is an open standard for connecting AI assistants to the systems where data lives, including content repositories, business tools, and development environments. As defined in the official MCP specification
MCP is like a “USB-C port” for AI applications - it provides a standardised way to connect AI models to various data sources and tools.
What MCP Enables:
Imagine asking Claude: “Check our user database for accounts created today, then analyse the application logs for any authentication errors, and create a summary report.”
With MCP, Claude can:
- Query your database directly
- Parse your log files
- Generate a comprehensive report
- Even create tickets in your project management system
The protocol has gained significant adoption, with OpenAI adding MCP support to their Agents SDK, and major companies like Wix integrating it for AI-driven development tools. Anthropic introduced MCP as a way to enable secure, two-way connections between Claude and external data sources.
Security Considerations:
I like learning new things, so I want to know MCP better; however, let’s also ponder the maturity of the MCP protocol, particularly its security aspects.
MCP enables seamless integration between LLM applications and external data sources, but this creates new security considerations. As outlined in the DataCamp MCP tutorial, security concerns include ensuring proper authorisation and authentication mechanisms are in place to prevent unauthorised access to external resources. MCP creates new attack vectors through indirect prompt injection vulnerabilities, where attackers could craft malicious messages containing hidden instructions.
Security experts recommend treating any MCP integration with caution and implementing proper access controls. For practical implementation guidance, the MCP implementation guide provides detailed examples of building secure MCP servers. Never give AI tools unrestricted access to critical systems.
My MCP Security Rules:
- Always review: Never let MCP tools execute commands without human approval
- Least privilege: Give MCP servers minimal necessary permissions
- Audit everything: Log all MCP interactions for security review
- Test in isolation: Experiment with MCP in sandboxed environments first
Practical Coding Philosophy
Surely, you will still need to use your brain, but the Generative technology makes coding a learning experience. The AI surely can save our coding and debugging time.
Here is my approach to using these tools effectively:
The Learning Approach:
- Use AI to understand new concepts, not just copy code
- Ask “why” questions: “Why did you choose this approach?”
- Experiment with AI suggestions in isolated environments
- Always read and understand generated code before using it
The Productivity Approach:
- Use AI for boilerplate code and repetitive tasks
- Let AI handle initial documentation and comments
- Use debugging assistance for tricky issues
- Generate test cases and edge case scenarios
The Quality Approach:
- Always review AI-generated code for security issues
- Test thoroughly, especially error handling paths
- Refactor AI code to match your project’s patterns
- Use version control to track AI-assisted changes
AI Tools Comparison
Based on my extensive experience with these tools, here’s an honest comparison:
Google Gemini
Strengths
- Excellent JavaScript/web development
- Superior debugging capabilities
- Strong API integration
- Good at complete, working solutions
- Free tier is generous
Limitations
- Less comprehensive documentation
- Can be inconsistent with complex logic
Best Use Cases
- Frontend development
- Ajax/API integration
- JavaScript debugging
- Quick prototyping
Pricing Free tier available Paid plans competitive
Security Standard data handling Good privacy controls
ChatGPT
Strengths
- Excellent explanations and teaching
- Great for algorithm understanding
- Strong documentation generation
- Wide language support
Limitations
- Often provides suggestions vs. complete solutions
- Struggles with a large codebase context
- Inconsistent code quality
Best Use Cases
- Learning new concepts
- Code documentation
- Algorithm explanations
- Quick syntax help
Pricing $20/month for Plus Usage-based API pricing
Security Data used for training Privacy concerns exist
Claude AI
Strengths
- Thoughtful, comprehensive solutions
- Excellent error handling patterns
- Strong architectural awareness
- MCP protocol support
- High-quality code generation
Limitations
- More expensive than alternatives
- Fewer free tokens
- Limited availability in some regions
Best Use Cases
- Production-quality code
- Complex system design
- Enterprise applications
Pricing $20/month for Pro Higher API costs
Security Strong privacy focus MCP security considerations
For Beginners:
- Start with Gemini for its generous free tier and excellent debugging
- Use ChatGPT for learning concepts and explanations
- Consider Claude when you need production-quality code examples
For Professional Development:
- Claude for architecture and complex business logic
- Gemini for frontend and API integration work
- ChatGPT for documentation and code explanation
For Enterprise/Security-Conscious Teams:
- Claude with carefully configured MCP servers
- Avoid storing sensitive data in any AI tool
- Implement code review processes for all AI-generated code
I recommend
After months of daily use, here’s what I recommend:
Start Simple: Begin with one tool and learn it well. I recommend Gemini for its balance of capability and accessibility.
Understand the Limitations: AI tools require source code as context and have input limits. They work best for focused tasks, not entire applications.
Develop Good Habits:
- Always review generated code
- Test thoroughly, especially edge cases
- Use version control for AI experiments
- Document what AI tools you used and why
Security First: Never blindly trust AI-generated code, especially for:
- Authentication and authorisation
- Data validation and sanitisation
- Financial or health-related calculations
- System administration tasks
And, be sure to track your code changes with Git to be able to roll back to the commit it was working well before :)
Conclusion
These AI coding assistants have genuinely transformed my development workflow, but they are tools, not magic solutions. Each has strengths and weaknesses, and the best approach is to use them thoughtfully as learning aids and productivity boosters.
Gemini excels at practical, effective solutions, especially in web development. ChatGPT serves as your patient instructor for grasping concepts. Claude offers enterprise-grade, well-considered implementations with robust error handling.
Remember - you are still the architect of your code. AI assistants can help you build faster and learn quicker, but understanding, testing, and maintaining your code remains your responsibility.
Did you like this post? Please let me know if you have any comments or suggestions about your experience with AI coding tools. I am always happy to learn from other developers’ experiences!
