Debugging with AI Assistance: Beyond Stack Overflow Copy-Paste
You've probably done this: paste an error message into ChatGPT, get back some confident-sounding fix, apply it, and watch it fail in a completely new way. The AI suggested using a library you don't have. Or it assumed your database schema looks different than it does. Or it just made something up that sounds plausible but doesn't exist.
Stack Overflow had this problem too, but at least those answers came with downvotes and angry comments. AI gives you hallucinations wrapped in certainty.
The problem isn't that AI is bad at debugging. The problem is that it's debugging someone else's code. It has no idea what your codebase actually looks like.
The Context Problem
When you ask an AI to help debug, you're giving it a tiny window into your system. A stack trace. Maybe a function. If you're thorough, a few related files. The AI then pattern-matches against every public repository it was trained on and gives you the statistically most likely answer.
This works great for common patterns. "Why is my React component re-rendering?" — AI can handle that. It's seen ten thousand variations of that problem.
But your actual bugs aren't common patterns. They're the weird stuff that happens when your authentication middleware interacts with your rate limiter, which checks a Redis cache that gets populated by a background job that reads from a Kafka topic that... you get the idea.
The AI doesn't know any of this. It can't. You didn't paste it into the prompt, and even if you tried, you'd hit token limits before you got halfway through the context.
What Actually Helps When Debugging
Here's what you need to debug effectively:
Feature boundaries. Is this bug in the authentication system? The payment flow? The notification service? Without knowing what features exist and where they live, you're just grepping randomly.
Dependency graphs. What does this code actually call? What calls it? You need the real runtime relationships, not just import statements.
Change history. When did this break? What changed recently in this area? Who changed it and what were they trying to fix?
Team knowledge. Who actually understands this code? Not who wrote it three years ago, but who's touched it recently and might know the gotchas.
Database state. What's the actual schema? What migrations have run? What's in there that the code doesn't expect?
You know all this stuff implicitly about your codebase. The AI doesn't know any of it.
Give up and ask a senior engineer who fixes it in ten minutes
The AI was confident every single time. It just had no idea what it was talking about.
The problem compounds when you're debugging integration issues. "This endpoint returns 500 but only sometimes" — the AI will suggest logging, error handling, try-catch blocks. All generic advice that might help but probably won't because you haven't told it about your distributed tracing setup, your database replication lag, or the fact that this only happens when the cache is cold.
What AI Actually Needs
AI debugging tools need to understand your codebase the same way a senior engineer would. Not by reading every file (good luck with that token limit), but by having an actual map of your system.
This is where code intelligence platforms like Glue come in. Instead of feeding your AI assistant random snippets, you give it a structured understanding of your codebase: where features live, how they connect, who owns what, and what's changed recently.
When you ask "why is authentication failing intermittently," an AI with access to Glue can actually see:
The authentication feature spans these 12 files
It depends on Redis, PostgreSQL, and an internal user service
The Redis connection pool changed 3 days ago
Two engineers have committed to this area recently
There's a known complexity spike in the session validation code
That's useful context. That's how humans debug.
The MCP Integration
Most AI coding assistants now support the Model Context Protocol (MCP), which lets them query external systems for context. Cursor, GitHub Copilot, Claude — they can all use MCP to pull in information beyond what's in your immediate files.
Glue provides an MCP server that your AI assistant can query. So when you're debugging and you ask "what changed in the payment flow recently," the AI can actually look it up instead of making something up.
This isn't magic. It's just giving the AI access to the same information you'd look up manually — code ownership, feature maps, dependency graphs, recent changes. But it happens automatically as part of the conversation instead of you having to context-switch to five different tools.
Real Debugging Scenarios
Let's get concrete. Here are debugging scenarios where AI with proper context actually helps:
The "it worked in dev" bug. Your staging environment behaves differently than local. Without context, AI suggests environment variables, configuration files, maybe Docker differences. With context, it can see that staging uses a different database schema version, there's a feature flag that's only enabled in staging, and the auth service has different rate limits. Now you're debugging the right thing.
The "this code is cursed" bug. You've got a function that everyone's afraid to touch. Lots of comments like "DON'T CHANGE THIS" and "HACK: but it works." AI with code intelligence can show you the full history: this code has high churn (gets modified frequently), low test coverage, and three different teams have made changes in the last month. It can also surface that there's newer code doing something similar that might be a better reference.
The integration bug. Two services aren't talking to each other correctly. Generic AI suggests checking network connectivity, API endpoints, authentication. AI with codebase context can map out the actual integration: Service A calls Service B through this BFF layer, which transforms the request format, and Service B expects a different schema version than what Service A is sending. Now you're looking at the schema mismatch, not rechecking your network configuration for the third time.
The "works on their machine" bug. Another engineer says it works for them, but you're seeing failures. Code intelligence shows that you're on different branches, they have a local database migration that hasn't been committed, and there's a feature flag difference in your environment configs. You'd eventually figure this out, but not by asking ChatGPT to read your error logs.
What Still Requires Humans
AI with context is useful, but it's not a magic debugging oracle. Here's what still needs human judgment:
Understanding business logic. AI can tell you what the code does. It can't tell you what it's supposed to do. If your discount calculation is wrong, the AI might make the code cleaner or more efficient, but it won't know that the business rules changed last quarter and the code hasn't caught up.
Making architectural decisions. AI can suggest refactors. It probably shouldn't decide whether to split your monolith into microservices or which database to use. These decisions have trade-offs that go beyond code patterns.
Knowing when to stop debugging and rewrite. Sometimes the bug is that the entire approach is wrong. AI will keep trying to fix the immediate problem. A human might recognize that you're trying to make a fundamentally flawed design work and suggest starting over.
Political debugging. Half of debugging is figuring out which team to talk to, who might be defensive about their code, and how to raise issues without starting wars. AI can't help with that.
The Future of This
We're going to see AI debugging get a lot better as code intelligence improves. Right now, most AI coding tools are blind to your actual system. They see files, not features. They see imports, not runtime behavior. They see commits, not team dynamics.
The tools that win will be the ones that combine LLM capabilities with deep codebase understanding. Not just "here's your error message" but "here's your error message, the feature it broke, the recent changes in that area, the team that owns it, and the three similar bugs that were fixed differently."
This isn't replacing human debugging. It's making the human faster. You still need to understand the problem, form hypotheses, and verify fixes. But you waste less time on "where does this error even come from" and "who can I ask about this code" and "what was working before."
Actually Try This
Next time you're debugging something non-trivial, try this experiment:
Debug normally. Use your usual tools, AI assistants, whatever.
Time how long it takes to identify the root cause.
Note how much time you spent gathering context (finding related code, checking history, figuring out dependencies).
I bet the context gathering is more than half your time. That's the part AI should be helping with, but only if it has access to actual code intelligence, not just text matching.
The engineers who are most effective with AI debugging aren't the ones with the best prompts. They're the ones who've given their AI assistants a real map of their codebase. Glue's MCP integration does this — your AI can query your actual system structure instead of guessing based on generic patterns.
Your bugs are specific to your code. Your debugging tools should be too.