Last Tuesday, I spent three hours chasing a memory leak in a Next.js application that was crashing our staging environment every six hours. The pattern was clear—memory usage would climb steadily until the process died—but the cause was invisible. No obvious infinite loops, no massive data structures, nothing in the profiler that screamed "this is your problem."

Out of frustration, I did something I’d never done before: I took the exact same debugging prompt—code snippet, error logs, system metrics, everything—and ran it through four different AI systems back-to-back. Claude, GPT-4, Gemini, and Grok. Same problem, same context, four completely different approaches.

What I learned in those twenty minutes changed how I think about AI-assisted debugging entirely.

The Prompt That Started Everything

Here’s what I fed each system:

Next.js app, memory usage climbing from 150MB to 2GB
over 6 hours then crashes. No obvious leaks in heap
snapshots. Using React Server Components, streaming
SSR, and edge runtime. Event listeners properly cleaned
up. What am I missing?

Simple, direct, frustrating. The kind of problem where you’ve already tried the obvious solutions and you’re starting to question your career choices.

Four Systems, Four Personalities

Claude came back like a senior engineer doing a code review. It asked clarifying questions first. "Are you caching API responses? How are you handling streaming cleanup? Have you checked for dangling promises in your server components?" It didn’t rush to conclusions. It wanted to understand the full system before offering theories.

When it finally suggested causes, they were architectural—focusing on how Next.js handles server component lifecycle and where streaming responses might not be properly closed. It pointed me toward the after() hook and suggested auditing my middleware chain for response streams that might not be terminating.

GPT-4 behaved like a textbook come to life. It gave me a structured, methodical breakdown: "Here are the seven most common causes of memory leaks in Next.js applications with streaming SSR." Each point had an explanation, example code, and specific things to check. Comprehensive, organized, slightly generic.

It suggested checking my database connection pooling, verifying that fetch requests in server components weren’t being cached indefinitely, and looking for event emitters that might not be garbage collected. Solid advice, but it felt like it was working from first principles rather than debugging instinct.

Gemini went for breadth over depth. It immediately started pattern matching across similar issues it had "seen" before. "This sounds like the Next.js 14.2 streaming bug that was patched in 14.2.3. Also possibly related to Vercel’s edge runtime memory management. Have you tried..."

It threw out five different possibilities rapid-fire, each one plausible, none of them developed deeply. Useful if you want to brainstorm many angles quickly, less useful if you want to methodically work through a single theory.

Grok surprised me by being the most opinionated. It basically said "This is almost certainly your middleware chain. Next.js middleware runs on every request in the edge runtime and if you’re not properly cleaning up, memory accumulates. Check your logging middleware first."

Bold, direct, and—as it turned out—partially right. My logging middleware was indeed holding references longer than it should have been, though that wasn’t the whole story.

The Pattern That Emerged

After working through all four responses, something clicked. Each AI wasn’t better or worse—each one was optimized for a different debugging strategy.

Claude excels at architectural debugging. When your problem is systemic, when the bug emerges from how different parts of your system interact, Claude’s tendency to ask questions and think holistically is invaluable. It’s the AI you want when you need to step back and reconsider your entire approach.

GPT-4 is your methodical checklist generator. When you need comprehensive coverage of all possibilities, when you want to make sure you haven’t missed something obvious, GPT-4’s structured, textbook approach prevents blind spots. It’s the AI you want when you need discipline, not intuition.

Gemini shines at pattern recognition across domains. When you’re debugging something that might be a known issue, or when you want to quickly explore many possible causes, Gemini’s breadth helps you cast a wider net. It’s the AI you want when you’re still in the hypothesis generation phase.

Grok cuts through ambiguity with confident theories. When you’re paralyzed by too many possibilities, when you need someone to just pick the most likely cause and run with it, Grok’s directness can be clarifying. It’s the AI you want when you need momentum over completeness.

The Real Discovery

Here’s what those twenty minutes taught me: using a single AI for debugging is like using only a hammer because it’s the best tool you own.

The most effective debugging session I’ve had in months happened because I stopped treating AI as "an assistant" and started treating different AIs as different modes of thought. When I needed systematic analysis, I consulted GPT-4. When I needed architectural insight, I asked Claude. When I got stuck on a hunch, I bounced it off Grok.

This isn’t about playing them against each other. It’s about understanding that different cognitive approaches reveal different aspects of the same problem. The memory leak wasn’t just one thing—it was a confluence of middleware behavior, streaming lifecycle issues, and subtle edge runtime quirks. No single AI caught all of it because no single debugging approach would have either.

The Practical Protocol

After this experience, I developed a new debugging workflow that leverages these differences deliberately:

Start with breadth using Gemini to generate hypotheses. Let it throw out five or six possible causes without committing to any single theory. This prevents premature narrowing of your investigation.

Move to structure with GPT-4o to systematically work through each hypothesis. Use its love of comprehensive checklists to ensure you’re testing each theory properly and not missing obvious checks.

Go architectural with Claude when structural issues emerge. If the problem seems to stem from how components interact rather than a single buggy function, Claude’s systems-thinking approach becomes invaluable.

Get decisive with Grok when you’re drowning in possibilities. Sometimes you just need someone to say "it’s probably this, check here first" to break analysis paralysis.

The key is treating this not as consensus-building but as perspective-gathering. You’re not looking for three AIs to agree on the answer. You’re collecting different lenses through which to view the same problem.

What This Means for How We Debug

The traditional debugging narrative is linear: identify the problem, form a hypothesis, test it, repeat until solved. But modern systems are too complex for purely linear thinking. You need multiple angles of attack simultaneously.

Different AI systems naturally provide those angles. Using Crompt AI to access multiple models in one interface means you’re not just getting different answers—you’re developing different ways of thinking about the problem in real-time.

This isn’t about outsourcing debugging to AI. It’s about expanding your cognitive toolkit by borrowing different reasoning styles as needed. The AIs aren’t solving the problem for you. They’re helping you think about it from angles your default mental model might miss.

The Debugging Blind Spot

Here’s what’s interesting: after running this experiment several more times with different bugs, I noticed a pattern in my own thinking. I was gravitating toward certain AIs based on my cognitive comfort zone, not based on what the problem actually needed.

When debugging frontend issues, I defaulted to Claude because I naturally think architecturally about UI systems. When debugging backend performance, I reached for GPT-4 because I prefer methodical profiling. But some of my biggest breakthroughs came when I forced myself to consult the AI whose approach felt least natural to me.

The memory leak? Grok’s aggressive "it’s probably your middleware" hunch was right, but I initially dismissed it because it felt too simple. Claude’s architectural perspective helped me understand why the middleware was leaking. GPT-4’s systematic approach ensured I tested the fix properly. Gemini pointed me to similar issues in the Next.js GitHub issues that confirmed my theory.

The bug wasn’t solved by one AI. It was solved by thinking through the problem from four different angles.

The Synthesis Problem

The hardest part of this approach isn’t accessing different AIs—it’s synthesizing their perspectives into actionable insight. Each system gives you a piece of the puzzle, but you’re still responsible for seeing the complete picture.

This is where tools like the Research Assistant become valuable. Not for the initial debugging, but for organizing and connecting the different theories you’ve collected. When you’ve got four different explanations of the same bug, you need a way to map their relationships and contradictions.

The Data Extractor helps when you’re comparing system metrics across different debugging sessions. The Document Summarizer becomes useful when you’re trying to distill lessons from multiple debugging attempts into principles you can apply next time.

But the synthesis itself? That’s still on you. The AIs can’t do that part—and they shouldn’t. That synthesis is where the learning happens.

The Meta-Lesson

Running the same debugging prompt through different AI systems taught me something bigger than debugging strategy. It revealed how much our choice of thinking tool shapes what we’re able to see.

If you only use one AI, you’ll only develop one mode of problem-solving. If you only use Claude, you’ll become great at architectural thinking but potentially weak at systematic elimination. If you only use GPT-4, you’ll be thorough but potentially miss bold hunches. If you only use Gemini, you’ll be great at generating possibilities but struggle to go deep on any single theory.

The real skill isn’t learning to use AI for debugging. It’s learning to think like different AIs do, using them to expand your own cognitive range rather than narrow it.

The Practice

Next time you hit a truly stubborn bug, try this: don’t ask just one AI for help. Ask three or four, deliberately choosing systems with different approaches. Don’t look for consensus—look for complementary insights.

Notice which perspectives you naturally gravitate toward and which ones feel uncomfortable. The uncomfortable ones are probably expanding your thinking the most.

Use platforms like Crompt that let you switch between models seamlessly, so you’re not managing multiple interfaces while trying to debug. The tool should facilitate perspective-gathering, not add cognitive overhead.

The goal isn’t to crowdsource debugging. It’s to develop the kind of multi-perspective thinking that the best senior engineers have naturally—the ability to look at the same problem from architectural, systematic, intuitive, and pattern-matching angles simultaneously.

The AIs just make that kind of cognitive flexibility more accessible to the rest of us.

That memory leak taught me more than how to debug Next.js. It taught me that the limitation isn’t the AI’s intelligence—it’s our tendency to use AI as an extension of our existing thinking rather than a way to think differently.

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Want to experiment with multi-perspective debugging? Try Crompt AI free and see how different models approach the same problem differently—because sometimes the bug isn’t in your code, it’s in how you’re thinking about it.