Why Plausible Code Can Still Be Wrong
AI gives you a piece of code and explains what it does. The explanation is clear. The code looks organised. It even includes a message saying the problem is fixed.
How much confidence should you place in that answer?
The difficulty is that an answer can look correct before anyone has shown that it works. This is what plausible means: something seems reasonable and believable.
When you build software with AI, you need to recognise the difference between a convincing answer and a result supported by evidence.
A clear answer can contain a wrong assumption
Imagine you are building the task app from the previous blog. You ask AI to let people mark tasks as finished.
AI creates a button that changes the task’s appearance. When you press it, the text becomes grey and a tick appears beside it.
That looks right.
However, after you close and reopen the app, the task appears unfinished again.
The change handled what appeared on the screen, but it did not save the completed status.
This can happen even when the code is neatly written and the explanation sounds sensible. The proposed solution may cover only part of what you meant.
Sometimes your request leaves an important detail unstated. Sometimes AI overlooks a requirement you did provide. Either way, the result needs to be compared with the intended behaviour.
Code can be valid without doing the right thing
There are different ways for code to be wrong.
Some mistakes prevent the software from running at all. Others allow it to run while producing an incorrect result.
Consider a shopping app that calculates the total price of two items:
- The first item costs $10.
- The second item costs $5.
- The app displays a total of $10.
The code might run without showing an error. It may simply leave the second item out of the calculation.
The computer has followed the instructions it received. Those instructions failed to produce the required result.
This is why “the app runs” and “the app works correctly” are different claims. Running successfully is useful evidence, but it answers only part of the question.
Correctness depends on the surrounding app
A piece of code can make sense on its own and still be unsuitable for your project.
For example, AI might add a new way to save tasks without realising that your app already has one. One screen could then read from the existing saved information while another uses the new version.
Each part may appear reasonable when viewed separately. Together, they can produce confusing behaviour.
A person might add a task on one screen and be unable to find it on another.
To judge a change, you need to consider how it fits the existing software:
- Does it use the information the app already stores?
- Does it follow the app’s existing rules?
- Does it change something another part relies on?
You will learn more about software structure later. For now, remember that code belongs to a larger system. Its surroundings affect whether it is correct.
Familiar examples may leave out important conditions
A solution may work for the example used to create it while failing in another ordinary situation.
Suppose AI creates a feature that finds a task using its name.
You try it with one task called “Buy groceries,” and it works.
Later, you create two tasks with the same name. When you mark one as finished, the app changes the other.
The first example did not reveal the problem because every task had a different name. The solution depended on an assumption that was easy to miss: task names would always be unique.
This is one reason a successful demonstration cannot answer every question about reliability. It shows what happened under the conditions of that demonstration.
The useful habit is to ask:
What conditions does this solution depend on?
You do not need to imagine every possible failure. Start by noticing ordinary variations in how someone might use the app.
Separate explanations, claims, and evidence
When you read an AI response, it helps to distinguish three things.
An explanation describes how something is meant to work
For example:
“When the user finishes a task, the app updates its saved status.”
This helps you understand the proposed design.
However, an explanation alone does not establish that the code behaves that way.
A claim states that something is true
For example:
“Completed tasks now remain finished after restarting the app.”
That is a statement about the result. It may be correct, but you still need to know what supports it.
Evidence shows what was actually checked or observed
For example:
“I marked a task as finished, closed the app, reopened it, and confirmed that the task still appeared finished.”
This gives you a concrete observation to assess.
It also has a limit: it describes one check under particular conditions. It does not automatically establish that every related situation works.
These distinctions help you ask a better follow-up question:
“What did you check, and what did you observe?”
That question is more useful than asking for reassurance alone.
Confidence should match the evidence
Different checks support different conclusions.
If the code has only been read, you have a review of the code.
If the app has opened successfully, you know it can start under those conditions.
If someone has completed the task journey and confirmed the saved result after reopening, you have evidence about that journey.
Each adds confidence, but each covers a different amount of ground.
Be careful when an answer moves from a narrow observation to a broad conclusion. “The app opened” does not support “all features work.”
The same principle applies when AI performs checks for you. Its report should make clear what it checked, what happened, and what remains untested.
Asking AI again does not settle the question
If you feel unsure, it is natural to ask:
“Are you sure this is correct?”
AI may reconsider the code and find a problem. That can be helpful. But another confident answer still needs support.
You can make the review more useful by asking specific questions:
- “Which assumptions does this solution make?”
- “Does this change save the result or only change the screen?”
- “What ordinary situation could make this behave incorrectly?”
- “Have you run this, or are you judging it by reading the code?”
These questions encourage a more precise account of the work.
If uncertainty remains, the next useful step is to investigate the behaviour. Repeated agreement does not replace that evidence.
You can make judgments without understanding every line
As a beginner, you may not yet be able to spot a mistake by reading code. You can still recognise important differences between the intended result and the actual result.
You can notice that a task disappeared, a total is wrong, or a button changed the wrong item.
You can also recognise when an answer offers reassurance without explaining what was checked.
Your technical understanding will grow. Until then, clear expectations and concrete observations give you a useful starting point for evaluating the work.
In the next blog, we will define what you need to understand yourself so you can delegate to AI while keeping control of the software you build.