As-salamu alaykum wa rahmatullahi wa barakaatuh!

You know, I've been working with AI and LLMs for debugging lately, and subhanAllah, I stumbled upon something that's been a complete game-changer for me. I call it the Expected Mechanism Comparison (EMC) method, and honestly, I'm so excited to share this with you because it's made my debugging process so much smoother.

How I Discovered This Approach

Let me tell you, I used to struggle with getting LLMs to help me debug effectively. I'd spend hours going back and forth with the AI, just telling it "fix this bug" or "this doesn't work" without giving it any real context. The AI would make changes, but often they'd break something else or miss the actual issue entirely. It was so frustrating! But Alhamdulillah, I finally realized my approach was completely wrong, and that's when everything changed.

What EMC Actually Is (And Why I Love It)

The whole idea is beautifully simple, and that's what makes it so powerful. Instead of just throwing my buggy code at an AI and saying "just fix this" (which was my old, terrible approach), I learned to break it down into two clear phases:

Phase 1: I tell the AI exactly what my code SHOULD do This is where I get really specific. I explain the expected behavior like I'm describing it to a friend who's never seen the code before. What should happen when a user clicks this button? How should the validation work? What edge cases should be handled?

Phase 2: I ask the AI to compare my actual code against those expectations Now here's the magic part - I give the AI my actual implementation and ask it to spot the differences between what I said it should do and what it actually does.

Why This Works So Well (From My Experience)

I've noticed that my old approach of just saying "debug my code" or "fix this bug" was setting both me and the AI up for failure. The AI would have to guess what I actually wanted the code to do, often making assumptions that were completely off. But with EMC, I'm being crystal clear about my intentions first. It's like giving the AI a roadmap before asking it to navigate.

The other beautiful thing is that this method has taught me to think more clearly about my own code. When I have to articulate exactly what my function should do before looking at the implementation, I often catch issues myself!

Let Me Show You How I Use It

Here's a real example: Let’s say you have a user validation function that is acting up:

Step 1 - Explain what it should do: "This function should take a user object, check if the email field exists and isn't empty, validate that it's a proper email format, and return true if everything's good or false with an error message if something's wrong. It should handle cases where the email is null, undefined, or just whitespace."

Step 2 - Ask for comparison: "Now please look at my actual code and tell me where it's not matching this expected behavior."

And subhanAllah, let the magic happen!

What Makes This Different (And Better)

There are other debugging approaches with LLMs, but EMC just hits different because:

It's systematic - I'm not randomly poking at code anymore
It's educational - I learn WHY my code is wrong, not just what to fix
It scales beautifully - Works just as well for a single function or a complex workflow
It’s SIMPLE!

My Tips for Getting the Most Out of EMC

Be really specific when describing expected behavior. Don't just say "it should validate the input." Say exactly what validation means - what formats are acceptable, what error messages should appear, how it should handle different scenarios.

Give context about the bigger picture. If this function is part of a user registration flow, mention that. The AI gives much better analysis when it understands the broader context.

Don't be afraid to iterate. Sometimes after the first round, I'll refine my expected behavior description and run through EMC again. It's not a one-and-done process.

The Bigger Picture

Honestly, this approach has changed how I think about working with AI tools in general. It's not about asking the AI to do everything for me - it's about creating a structured conversation where I'm clear about my intentions and the AI can provide focused, helpful analysis.

I've been using EMC since recently, and Alhamdulillah, my debugging time has shortened. More importantly, I'm learning and improving as a developer because I'm forced to think clearly about what my code should actually do.

What This Means for All of Us

I really believe we're in this amazing era where AI can amplify our abilities as developers, but only if we learn how to communicate with these tools effectively. EMC is just one example of how a little structure in our approach can make a huge difference.

The future isn't about AI replacing us - it's about us getting really good at collaborating with AI. And methods like EMC show us what that collaboration can look like when done thoughtfully.

I hope this helps you as much as it's helped me! If you try EMC out, I'd love to hear about your experience. May Allah make it beneficial for us, ameen!

Feel free to share your own debugging discoveries. We're all in this learning journey together, Inshaa Allah!

Bonus Productivity Tip for Developers

If you're a developer looking to work faster and more efficiently, check out our tool VoiceHype — a powerful SaaS product built specifically for developers who want to speak instead of type. With VoiceHype, you can not only generate accurate transcriptions by voice, but also optimize and interact with your transcripts using advanced LLMs like Claude. It supports multiple smart modes tailored to different tasks and use-cases. Alhamdulillah, it's available as a VS Code extension — just search for "VoiceHype" on the marketplace and give it a try. It's made with developers in mind, and we hope you'll find it truly useful, InshaaAllah.

Checkout https://voicehype.ai.

The Expected Mechanism Comparison (EMC) Method: A Revolutionary Approach to AI-Assisted Debugging