The Hard Part of AI Coding Isn’t Using AI. It’s Knowing When Not to Trust It.

In the world of AI coding tools, the hard part is not using the AI, but knowing when not to trust it. While AI can significantly reduce friction in the development process, it is crucial to decide where it fits in the workflow to avoid quietly handing over judgment. AI excels at streamlining tasks like tracing unfamiliar code, generating boilerplate, explaining APIs, proposing refactors, writing tests, debugging, and suggesting potential causes for bugs.

This can make it feel like a super-fast technical collaborator. However, speed can lead to a trap where plausible answers may feel finished even before they have been verified. Plausible does not mean correct. AI coding tools are highly skilled at producing answers that look right, with clean code and convincing explanations. However, sometimes the model invents incorrect information, such as using outdated formats, misunderstanding library versions, ignoring edge cases, weakening security checks, or fixing only symptoms while leaving the actual bug intact.

The most dangerous mistakes are not always the ones that immediately crash the application. Sometimes, these are issues that appear to work fine, like an authentication change allowing valid users to sign in while bypassing authorization checks, a database migration succeeding in a development environment but failing in production, or a generated test passing due to reproducing the same incorrect assumption as the implementation.

Even after the app starts and tests pass, this does not guarantee correctness. Due diligence is part of the workflow. If an AI agent suggests a library supports something, it's important to check the documentation. If the agent modifies authentication code, the implications of security must be inspected. When the agent writes a migration, the SQL should be read.

The tests should be run, and the dependencies added by the model should be understood. Asking why a proposed fix works, rather than simply verifying if the application starts, is essential. While generated code should be treated with the same scrutiny as code from any other source, and occasionally more, because the model cannot be held accountable for the result, a human teammate can explain the assumptions behind a decision, remember conversations that changed requirements, and recognize when a technically valid implementation could create a terrible user experience.

A model can explain its output, but that explanation is generated after the fact and is not proof that the model started with correct assumptions. Verification should match the risk of the change. For instance, a developer does not need to conduct a security audit if an agent changes a button's padding, but changes involving authentication, authorization, payments, destructive operations, personal data, infrastructure, database schemas, or production configuration require substantially more attention.

The higher the potential cost of being wrong, the less appropriate it is to accept "looks good" as evidence. Passing tests can create false confidence, as AI agents can write tests that confirm the implementation behaves exactly as the agent expects. However, this does not prove that the agent's expectations align with the product requirement.

If the same model writes both the implementation and the tests, they might share the same misunderstanding. Suppose the requirement is that only account owners can delete a project. The agent might interpret "account owner" as any authenticated user associated with the account, leading to the generation of authorization logic and a complete test suite based on this interpretation.

Even if every test passes, the implementation remains incorrect. Good verification questions more than whether the generated tests are green: Are the tests reflective of the actual requirements? Are failure paths covered? Are permissions tested from various user roles' perspectives? Are boundary conditions included? Would the tests fail if the implementation were subtly weakened?

Were existing tests changed or removed simply to make the implementation pass? While technically correct, AI-generated solutions can still be wrong for the project. They might introduce unnecessary libraries, replace straightforward functions with elaborate abstractions, or use dependencies incompatible with the deployment environment.

Sometimes, AI proposes solutions that are technically valid but not justified by the problem. Good engineering is about selecting a solution whose complexity is justified by the problem. AI can provide options, but it cannot own the tradeoffs. Choosing AI tools also requires judgment. Developers should not chase every new coding assistant that appears, as each week seems to bring another agent with impressive demos and promises to revolutionize software development.

Some tools are genuinely useful, excellent in specific contexts, and mediocre in others. Some might even disappear six months after developers have adapted their workflows around them. A model might excel at exploring a large codebase but become unnecessarily verbose when implementing small changes. Another model could generate polished interfaces but fail to consider user experience.