AI coding assistants are getting better at reading codebases.
They can follow imports, explain a data flow, identify duplicated logic, and suggest changes that fit the patterns already used in a project. Give them enough context and they can often describe the system more clearly than a tired developer opening the repository for the first time.
That creates a new kind of mistake.
The code can be technically correct. The architecture can look consistent. The tests can pass. And the change can still be wrong for the product.
This is not the same as saying that AI cannot understand software. It can understand a surprising amount of software. The problem is that a product contains decisions that are not fully represented in the code.
A system is more than its implementation
When developers say “the system,” they often mean the code, database, APIs, deployment setup, and tests.
Users experience something larger:
the sequence of steps they have to remember
the amount of uncertainty before an action
the language used by the interface
the consequences of making a mistake
the reason a feature exists in the first place
Those details may be scattered across support tickets, design discussions, analytics, documentation, and the team’s shared memory. Some may not be written down anywhere.
An AI assistant can inspect the visible structure and still miss the invisible contract.
That is how it can get the system right and the product wrong.
Local correctness is not product correctness
Imagine a settings page with an option that looks unnecessarily complicated.
An AI assistant may suggest removing it, combining two controls, or replacing the flow with a cleaner component. From the code’s point of view, the suggestion may be sensible. The component becomes smaller. The state model becomes easier to follow. The interface looks more modern.
But perhaps the two controls represent two different decisions for different users. Perhaps support teams rely on the distinction when diagnosing problems. Perhaps an older integration sends both values separately. Perhaps the confusing wording is temporary because the product team is waiting for a larger redesign.
The code does not necessarily reveal any of that.
The proposed change is locally correct. It is product-incorrect.
This distinction matters more as AI becomes capable of producing polished diffs. A broken suggestion is easy to reject. A clean suggestion that violates an unstated product rule is much harder to notice.
The most dangerous answer is the plausible one
AI-generated code is often criticized for being buggy. That is a real risk, but obvious bugs are not always the most expensive failures.
The more subtle failure is plausibility.
The code uses the project’s naming conventions. It follows the framework’s recommended patterns. It adds a reasonable test. It even improves a few nearby lines while making the requested change.
Everything looks professional.
The problem may only appear later, when a user takes an unexpected path, when an existing workflow needs to be extended, or when another team discovers that a behavior was intentional rather than accidental.
Plausible code lowers the reviewer’s guard. That is why AI-assisted development needs stronger questions, not just better code generation.
Ask what the product is protecting
Before accepting a generated change, ask a question that code review often skips:
What existing user promise is this product protecting?
The promise does not have to be dramatic. It might be:
users can undo an action without losing their work
an imported file is never modified in place
a beginner can complete the first task without understanding the underlying format
a creator can compare two versions before choosing one
a failed operation explains what happened instead of silently doing nothing
These promises shape implementation decisions. They also explain why a seemingly inelegant piece of code may be there.
If the prompt does not mention the promise, the assistant may optimize for simplicity while removing the behavior that made the product trustworthy.
Creative tools expose the gap quickly
Music software makes this difference easy to see.
A tool can analyze a track, display notes, or generate a useful starting point. That does not mean the result answers the creator’s real question.
Someone may open a midi player online because they want to hear how a sequence feels before editing it. The visible task is “play this file.” The actual product need may be “help me decide whether this idea is worth developing.”
Another creator may use a live bpm detector while preparing a sample. The number matters, but it is only useful in context. Is the track being counted at half-time? Is the tempo intentionally loose? Does the detected value help the creator make a decision, or does it simply add another number to the screen?
The same principle applies to software development. A generated implementation may satisfy the explicit request while missing the decision the user is trying to make.
Context is not the same as volume
When an AI suggestion feels wrong, the usual response is to add more context.
More context can help, but a larger prompt is not automatically a better prompt. A model can receive thousands of lines of code and still miss one important constraint.
Useful context has direction. It explains:
what must remain unchanged
which behavior is intentional
who owns the decision
what failure looks like
what tradeoff matters most
what the user should be able to understand afterward
Compare these two requests:
Refactor this form to make it simpler.
And:
Reduce duplicated rendering logic, but preserve the distinction between draft and submitted values. Users must be able to leave and return without losing a draft. Do not change the validation messages.
The second prompt is not necessarily longer by much. It is simply more honest about the product.
Use AI to challenge assumptions
The best use of a second opinion is not asking for a replacement decision. It is asking the assistant to expose the assumptions behind a decision.
Useful questions include:
What behavior does this change assume?
Which users could be confused by it?
What existing behavior might this break?
What is the smallest implementation that tests the idea?
Which parts of the proposal are necessary, and which are cleanup?
What would make this solution inappropriate six months from now?
These questions turn AI from an answer machine into a review partner.
They also make it easier to disagree with the output. Instead of saying “the AI suggested this architecture,” the team can discuss the assumptions, risks, and evidence behind it.
Do not confuse a working demo with validation
AI makes prototypes cheap. That is one of its biggest advantages.
A team can explore several interface ideas, generate sample data, or test a workflow before committing to a full implementation. But a prototype answers a limited question: can this idea be made to work?
It does not answer:
will users understand it?
will they use it repeatedly?
does it fit the rest of the product?
does it create more support work than it removes?
is the new complexity worth the benefit?
The faster the prototype is produced, the more important it becomes to separate technical feasibility from product validation.
Human judgment moves to the edges
AI-assisted development changes where developers spend their time.
Less time may be needed for typing boilerplate or searching for a familiar implementation. More time is needed before generation, when the problem is framed, and after generation, when the result is judged.
Before the prompt, someone has to understand the user’s situation. After the diff, someone has to decide whether the change belongs in the product.
That judgment includes technical questions, but it is not limited to them. It includes language, trust, discoverability, accessibility, reversibility, and maintenance.
The human role is not disappearing from the workflow. It is moving toward the places where context and consequences matter most.
A useful second opinion leaves room for disagreement
The phrase “second opinion” can imply that one answer is waiting to be selected.
In practice, a useful second opinion may simply make the original decision clearer. It may reveal that the problem was framed too narrowly. It may show that two reasonable solutions serve different users. It may confirm that the existing awkward behavior is protecting something important.
That is a valuable outcome even when no generated code is used.
The goal is not to make AI agree with the first idea. The goal is to make the team better at seeing the decision from another angle.
The point
AI often gets the system right because it can inspect structure, patterns, and dependencies.
It often gets the product wrong because product meaning lives partly outside the code.
The answer is not to stop using AI. It is to give it better constraints and review its suggestions against the promises the product makes to real people.
A clean diff is evidence that a change can fit the codebase.
It is not evidence that the change belongs in the product.
Top comments (0)