Can AI Write Reliable Code? What to Check Before You Run It

What an AI coding assistant actually does

At the simplest level, a coding assistant predicts useful code from the information it has been given. That information may include your written request, the current file, nearby files, repository instructions, error messages, tests and tool output.

Depending on the product and permissions, the assistant may do more than fill in a line. It may:

The important phrase is the information it has been given. A model may be excellent at Python and still misunderstand your application because it has not seen the database constraint, the deployment environment or the decision recorded in a meeting six months ago.

A coding assistant does not inherit the full mental model of the team. It builds a temporary model from the context it can access.

Why incorrect code can look completely professional

Bad human code often looks bad. Names are confusing, formatting is inconsistent and the writer appears uncertain. AI-generated code may have none of those warning signs.

It can be tidy, commented and confidently structured while still being wrong.

These two columns overlap, but they are not the same. Readability makes code easier to inspect. It does not prove that the code does the right thing.

The original HumanEval research on code-generating language models measured functional correctness by running tests, not by asking whether a solution looked convincing. That distinction remains essential. Code is unusually testable compared with ordinary prose, and we should use that advantage.

A code block that has not been executed is still a claim about what it will do.

The most common failure is not ignorance. It is missing context.

Developers sometimes describe an AI mistake as hallucination. That can be accurate, especially when the tool invents an API, package or configuration option. But many failures are more ordinary: the assistant receives an incomplete picture and fills the gaps with a reasonable guess.

Imagine asking for a password-reset endpoint. The assistant may need to know:

If none of this is stated, the tool may create a technically plausible endpoint that violates the project’s actual security model.

Repository-wide agents can reduce this problem by reading more files and running tools. They do not eliminate it. Some requirements are not stored in code. Others are stored in old documents, operational knowledge or people’s heads.

The safer question is not “Does the AI know how to build password reset?” It is “Has the AI been given enough of our password-reset requirements to propose a change we can evaluate?”

Tests turn a confident answer into something you can challenge

Testing is not a ceremony added after coding. It is how a team makes expectations executable.

For the appointment-date example, one test that checks “yesterday is rejected” is not enough. A useful test set would include:

AI is often good at generating candidate tests. That is valuable, but the same missing context can affect the tests. A tool may write tests that merely confirm its own mistaken interpretation.

The person reviewing the change should ask a simple question: Which real-world failure would this test catch?

Tests are strongest when they come from requirements, past incidents and known risks—not only from the implementation that happens to exist.

The dependency trap: a one-line import can carry a large decision

Generated code frequently solves a problem by adding a library. That can be sensible. It can also introduce more risk than the original feature.

GitHub’s own guidance for reviewing AI-generated code warns reviewers to verify suggested dependencies and watch for packages that do not exist or have suspicious names.

A generated import statement is not a neutral convenience. It changes what your software trusts, downloads, updates and may eventually expose to users.

Security is not something you ask for after the code works

A common workflow is:

1. Ask the AI to build the feature.
2. Confirm that the happy path works.
3. Later, ask whether there are security problems.

That order is backwards for features involving authentication, payments, file uploads, personal data, permissions or external commands.

Security decisions shape the design. They affect which data is accepted, where it is stored, who can act, what is logged and how failures are contained.

NIST’s Secure Software Development Framework treats security practices as part of the development lifecycle rather than a final scan. The same principle applies when the first draft comes from an AI tool.

Modern coding agents can help find vulnerabilities and propose patches, but even security-focused systems are designed around validation and human review. A suggested patch remains a change to be examined, tested and approved.

Better coding requests describe the environment, not only the feature

“Build a login page” leaves almost every important decision open. A stronger request defines the boundaries of the work.

The stronger version provides:

• the framework and project context;
• existing components that must be reused;
• explicit prohibitions;
• a business rule;
• a request for assumptions;
• and a staged workflow.

Useful coding prompts often follow this order:

This may feel slower than asking for the final code immediately. In practice, it reduces rework because misunderstandings are exposed before they spread across several files.

A practical review ladder for AI-generated code

Not every change needs the same level of scrutiny. Renaming a local variable is different from changing payment authorization. The review should match the consequence.

OpenAI’s guidance for coding agents emphasizes that users should inspect the work through diffs, logs and test results, then manually review and validate generated code before integration and execution. GitHub similarly advises functional checks, context review, dependency scrutiny and collaborative review.

Can someone who does not code build an application with AI?

Yes—up to a point that depends on the application.

A non-developer can now create prototypes, personal tools, simple websites, small automations and internal forms that previously required much more technical help. That is a genuine expansion of access.

The difficulty arrives when the application must survive real users, private data, payments, legal obligations, integrations and future maintenance.

The issue is not that non-coders are forbidden from building. It is that every builder needs a way to recognize when the project has crossed beyond their ability to verify it.

AI lowers the cost of producing code. It does not automatically lower the consequence of a mistake.

Where AI coding tools are most useful today

The strongest uses are often not “build the whole product for me.” They are smaller tasks where the result can be checked quickly.

The common feature is fast feedback. The task is bounded, the output can be inspected and mistakes are less likely to hide inside a large autonomous change.