Treat AI output as a draft
GitHub’s 2025 data shows generative AI and agentic workflows becoming ordinary engineering practice. Stack Overflow’s survey similarly shows broad adoption. Popularity, however, is not proof of correctness. Generated code should enter the same review system as human-written code.
Begin with a written task: desired behaviour, constraints, existing architecture, examples, and definition of done. Ask for a plan before a large change. Smaller, inspectable changes are easier to review than a one-shot rewrite.
Use a verification loop
GitHub’s guidance for reviewing AI-generated code starts with compilation, tests, and static analysis, then asks developers to verify context, intent, quality, dependencies, and AI-specific failure modes. Turn this into a repeatable loop.
- Specify: state the problem, constraints, interfaces, and non-goals
- Generate: request the smallest coherent change
- Inspect: read every changed line and dependency
- Test: run automated checks and exercise failure paths
- Explain: describe why the solution works in your own words
- Record: keep useful decisions in code, tests, or documentation
Protect the boundaries
Do not paste credentials, confidential data, proprietary code, or personal information into an AI service without appropriate organisational approval and controls. Verify packages before installation; hallucinated or malicious package names are a known risk. Review licensing and public-code matches when relevant.
For authentication, payments, permissions, encryption, destructive commands, and sensitive data, use a higher standard of review. An answer that looks conventional can still be unsafe in the specific system.
Keep learning below the abstraction
If AI writes a database query, understand joins, indexes, transactions, and injection risk. If it produces a React component, understand state, rendering, accessibility, and browser behaviour. If it configures cloud infrastructure, understand identity, networking, cost, and failure recovery.
A useful test is simple: can you debug the result when the AI is unavailable or confidently wrong? If not, reduce the scope and study the missing concept before expanding the feature.
Use AI to increase responsibility, not just output
The strongest use of AI is not producing more unreviewed code. It is creating space for better tests, clearer documentation, deeper user understanding, and more iterations. Speed only becomes valuable when the work remains dependable.
Common questions
Is AI coding suitable for beginners?
It can support learning, but beginners should avoid accepting code they cannot explain. Use it to ask questions, compare approaches, generate exercises, and review your reasoning.
Will AI replace software developers?
Roles and workflows are changing, but software work still requires problem framing, system judgement, verification, collaboration, and accountability. Specific outcomes cannot be predicted or guaranteed.
Should AI-generated code be disclosed?
Follow the policies of your employer, client, school, and tools. Disclosure may be required, especially where authorship, licensing, assessment, or sensitive systems are involved.
What is the most important AI coding skill?
Verification: the ability to determine whether output matches intent, works under realistic conditions, and is safe to maintain.
Sources and further reading
References support the technical background. Learning plans, checklists and interpretations are KORLANCE editorial guidance. Read our editorial policy.