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Ashish Sharda
Ashish Sharda

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Building Vajra: My Journey Creating an AI Coding Assistant

#ai #coding #chatgpt #productivity

Introduction

A few weeks ago, I found myself constantly switching between my code editor and ChatGPT, asking for help with debugging, API design, and code optimization. I thought: "What if I could have a specialized AI coding assistant right inside ChatGPT?"

That's how Vajra - AI Coding Assistant was born. Today, I'm sharing my journey of building and launching it on the GPT Store.

Vajra

The Problem I Wanted to Solve

As developers, we face similar challenges daily:

  • Writing boilerplate code repeatedly
  • Debugging obscure errors
  • Designing APIs with best practices
  • Refactoring legacy code
  • Learning new frameworks and languages

While tools like Cursor and GitHub Copilot are excellent, I wanted something that could:

  1. Work directly in ChatGPT (no additional installations)
  2. Provide detailed explanations alongside code
  3. Handle complex architectural questions
  4. Support multiple programming languages

The Design Philosophy

I wanted Vajra to be more than just a code generator. Here's what I focused on:

1. Quality Over Speed

Every response should be production-ready, not just "works on my machine" code. This meant:

  • Following language-specific conventions
  • Including proper error handling
  • Adding inline documentation
  • Providing test cases when relevant

2. Teaching, Not Just Telling

Good developers understand why code works, not just that it works. Vajra explains:

  • The reasoning behind architectural decisions
  • Trade-offs between different approaches
  • Potential edge cases and how to handle them

3. Real-World Readiness

Code should include:

  • Security considerations
  • Performance optimization tips
  • Scalability best practices
  • Industry-standard patterns

Building Process

Phase 1: Research

I spent time studying:

  • How existing AI coding assistants work
  • Common pain points developers face
  • Best practices across different languages
  • Enterprise coding standards

Phase 2: Instruction Design

This was the most critical part. I had to define:

  • Core capabilities: What should Vajra excel at?
  • Communication style: How should it explain things?
  • Boundaries: What should it avoid?
  • Special features: What makes it unique?

Phase 3: Testing

I put Vajra through rigorous testing:

  • ✅ Simple function generation
  • ✅ Complex debugging scenarios
  • ✅ API design challenges
  • ✅ Algorithm explanations
  • ✅ Multi-language support

Real-World Examples

Example 1: Email Validation

Prompt: "Create a Python function that validates email addresses using regex"

Vajra delivered:

  • Clean, RFC 5322 compliant regex
  • Proper type hints and docstrings
  • Example usage with test cases
  • Suggestions for enhancements (DNS verification, Pydantic integration)

Example 2: Debugging

Prompt: "Help me debug this JavaScript loop"

Vajra:

  • Identified the exact bug (off-by-one error)
  • Explained why it was wrong
  • Provided the fix with comments
  • Offered a better alternative using modern JavaScript methods

Example 3: API Design

Prompt: "Design a REST API for a todo list app"

Vajra created:

  • Complete OpenAPI 3.1 specification
  • CRUD operations with advanced features
  • Pagination, filtering, and sorting
  • Optimistic concurrency control with ETags
  • Real cURL examples
  • Error handling standards

Key Lessons Learned

1. Iteration is Everything

My first version was too verbose. I refined the instructions multiple times based on testing to find the right balance between detail and conciseness.

2. Context Matters

Different developers need different things. Some want quick answers, others want deep dives. Vajra adapts based on the complexity of the question.

3. Best Practices Are Universal

Whether it's Python, JavaScript, or Go, principles like clean code, proper error handling, and security consciousness apply everywhere.

4. Testing Reveals Gaps

Every test case revealed something new. The debugging challenge showed me Vajra needed to be more proactive about suggesting modern alternatives.

The Result

After extensive testing and refinement, Vajra is now live on the GPT Store! It's helping developers:

  • Write cleaner code faster
  • Debug with confidence
  • Design better APIs
  • Learn new languages and frameworks
  • Follow industry best practices

What's Next?

I'm continuously improving Vajra based on user feedback. Some ideas I'm exploring:

  • Enhanced framework-specific guidance
  • More specialized domain knowledge (DevOps, ML, etc.)
  • Integration with coding standards documentation
  • Expanded debugging scenarios

Try It Yourself

If you're a developer looking for an AI coding companion, give Vajra a try! It's completely free to use with ChatGPT Plus.

🔗 Try Vajra - AI Coding Assistant

Final Thoughts

Building Vajra taught me that creating effective AI tools isn't just about prompts—it's about understanding real user needs, iterating relentlessly, and prioritizing quality over everything else.

Whether you're building your own GPT or just exploring AI tools, remember: the best tools are the ones that make users better at what they do, not just faster.

Happy coding! 🚀

What are your thoughts on AI coding assistants? Have you built any GPTs? Share your experiences in the comments below!

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