I asked Claude to explain a function yesterday. Fifteen lines of Python that calculated user permissions based on role hierarchies and feature flags. The AI gave me a beautiful explanation—clear, structured, technically accurate.
It was also completely useless.
The explanation covered what each line did. It described the data structures. It even pointed out potential edge cases in the logic. What it didn't tell me was why this function existed in the first place, why it was structured this way, or what problem the original developer was actually solving.
This is the gap that's quietly breaking developers who rely too heavily on AI code assistance. The models explain syntax brilliantly. They fail catastrophically at explaining purpose.
The Illusion of Understanding
Here's what happened: I was debugging a permissions issue in a legacy codebase. Users with certain role combinations were seeing features they shouldn't access. I found the function that seemed responsible and asked AI to explain it.
The response was impressive. It walked through the conditional logic, explained the data flow, and identified that the function was checking user roles against a feature flag matrix. Everything it said was technically correct.
But I still had no idea why the permissions were broken.
The AI couldn't tell me that this function was originally written to handle a specific customer's complex org structure. It didn't know that the nested conditionals were workarounds for a limitation in the role management system. It had no context about the three refactors that had touched this code, each one adding layers of complexity to handle new edge cases.
It explained the what perfectly. It missed the why entirely.
The Context Problem
Code doesn't exist in isolation. Every function is embedded in layers of context:
Historical context: Why was this written this way? What constraints existed when it was created? What problems was it solving?
Architectural context: How does this fit into the larger system? What assumptions does it make about surrounding code? What contracts does it depend on?
Business context: What real-world problem does this solve? Which customers or use cases drove this design? What tradeoffs were made and why?
Cultural context: What were the team's coding standards when this was written? What patterns were they following? What conventions have since changed?
AI has access to none of this. It sees your code as a text file, not as the artifact of dozens of decisions made by specific people solving specific problems under specific constraints.
This creates a dangerous illusion. The explanations look comprehensive. They sound authoritative. They're formatted clearly with examples and edge cases. But they're explaining a shadow of the actual code—the syntax without the story.
When Explanation Becomes Misleading
The real danger isn't that AI explanations are incomplete. It's that they're confidently incomplete in ways that actively mislead developers.
I've watched junior developers use AI tools to understand complex codebases, and the pattern is consistent: they get excellent explanations of how the code works, then make terrible decisions about what to change because they don't understand the context those changes will affect.
They see a function with nested conditionals and AI correctly identifies that it could be simplified. What AI doesn't know is that those conditionals were deliberately kept separate to make debugging easier for a specific team member who was colorblind and needed clear visual separation in error logs.
They find a complex data transformation and AI accurately suggests a more elegant approach using functional patterns. What AI can't see is that the current implementation was chosen specifically because it performed better under the production load profile, which looked nothing like typical usage.
They discover an apparently redundant check and AI correctly notes that it seems unnecessary. What AI doesn't realize is that check was added after a production incident where a race condition caused data corruption, and removing it would reintroduce the vulnerability.
The explanations are accurate about the code itself. They're completely wrong about the system the code exists within.
The Teaching Problem
This becomes especially problematic for developers learning from AI explanations. They're building mental models based on syntactic correctness without contextual understanding.
When I learned to code, I learned from people. Those people didn't just explain what the code did—they explained why it existed, what problems it solved, and what mistakes they'd made along the way. The code came wrapped in stories that conveyed context.
AI strips away the stories and gives you sanitized technical explanations. This creates developers who understand syntax deeply but struggle with system thinking—who can explain every line but can't explain why the system is designed this way instead of the ten other ways it could have been designed.
I see this in code reviews constantly. A developer proposes a change that technically improves the code they're looking at, but breaks assumptions elsewhere in the system. When asked why they made that choice, they explain that AI suggested it and the explanation made sense. They're not wrong—the local explanation did make sense. But optimizing locally without understanding the global context is how you accumulate technical debt.
What AI Actually Does Well
This isn't an argument against using AI for code explanation. It's an argument for understanding what AI explanations actually give you.
AI excels at syntax explanation. If you need to understand what a specific function does in isolation, AI will explain it clearly and accurately. If you need to know what a particular pattern or data structure does, AI will break it down precisely.
AI is excellent for learning new languages or frameworks. When you're trying to understand how a specific feature works in a language you don't know well, asking an AI assistant to explain the syntax and standard patterns is genuinely useful. The context that matters there is the language itself, not your specific codebase.
AI works well for explaining well-documented, widely-used patterns. If your code uses standard design patterns or common algorithms, AI can explain them effectively because it has seen thousands of similar implementations. The context is standardized.
Where AI breaks down is explaining code that exists for non-obvious reasons. Code that's a result of specific constraints, specific business logic, specific architectural decisions, or specific historical accidents. Which, in real-world codebases, is most of the code that actually matters.
The Missing Layer
What we need isn't better AI explanations. What we need is a system for capturing the context that AI can't infer.
The best codebases I've worked with had extensive comments—not explaining what the code did (the code itself did that), but explaining why it existed. Comments that said things like:
"This seems redundant but we need it for the edge case where users have overlapping role assignments from different orgs."
"We considered using a hash map here but benchmark testing showed this linear search performed better at our typical data volumes."
"This conditional structure matches the business logic document in Confluence. Don't refactor without checking if that spec has changed."
These comments don't explain syntax. They explain context. And context is what makes code comprehensible.
When I use AI to explain code now, I use it as a starting point, not an endpoint. I ask it to explain the syntax, then I go hunting for the context. I look at git history to see what problem the original commit was solving. I search Slack for discussions about this code. I find the tests to understand what behaviors were considered important. I check documentation to see if there's business logic I'm missing.
The AI explanation tells me what I'm looking at. The context tells me what I'm actually dealing with.
The Tool Integration Strategy
Here's how I've started using AI for code explanation in a way that accounts for its limitations:
Step one: Get the syntactic explanation. Use Claude Sonnet 4.5 or a similar model to explain what the code literally does. This is what AI handles well—pure syntax and structure.
Step two: Ask about context gaps. Explicitly ask the AI what context it's missing. "What information would you need to fully understand why this code exists?" The AI often can't answer this, but asking forces you to think about it.
Step three: Cross-reference with documentation. Use document analysis tools to pull relevant context from your project docs, Confluence pages, or design specs. AI can help find related documentation but can't synthesize it with code understanding.
Step four: Check git history. Look at the commit that introduced this code and the commits that modified it. The commit messages and PR discussions contain contextual information AI can never access.
Step five: Verify with humans. If the code touches critical paths or has unclear motivation, ask someone who worked on it or has context about that area. Human context beats AI explanation every time.
This is more work than just asking AI to explain the code. But it's the only way to actually understand what you're looking at.
The Real Value Proposition
AI code explanation tools aren't useless. They're valuable for exactly what they do—explaining syntax clearly and quickly. The problem is when developers treat them as comprehensive understanding tools instead of first-pass syntax parsers.
If you're learning a new language or framework, AI explanations are genuinely helpful. If you're trying to understand a standard algorithm or pattern, AI works well. If you need a quick overview of what a function does before you dive deeper, AI saves time.
But if you're trying to understand why code exists, how it fits into the larger system, or what constraints shaped its design, AI will confidently give you half the picture and leave you thinking you understand when you don't.
The most dangerous outcome isn't AI giving wrong explanations. It's AI giving technically correct but contextually incomplete explanations that feel comprehensive. You walk away thinking you understand the code when you've only understood its syntax.
The Path Forward
The future of AI code explanation isn't better models that can infer more context. It's systems that make context explicit and accessible alongside the code.
We need tools that connect code to the conversations that shaped it, the decisions that constrained it, the incidents that modified it, and the business logic that drove it. We need AI that says "I can explain the syntax, but here are the context sources you should check" instead of pretending the syntax explanation is sufficient.
We need to train developers to understand the difference between syntactic understanding and contextual understanding—to know when they've learned what the code does versus why it exists.
And we need to get better at documenting context in the first place. The best AI explanation tool in the world can't help you if the context was never captured anywhere. Write comments that explain decisions, not syntax. Maintain decision logs. Document the business logic that drives technical choices.
AI will continue to get better at explaining code. But it will never understand your specific codebase's specific history without that history being explicitly captured and made available.
Until then, use AI for what it's good at—explaining syntax clearly and quickly. But never mistake that explanation for actual understanding.
The code you're looking at is the answer to a question. AI can tell you what answer someone wrote. Only context can tell you what question they were answering.
-Leena:)
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