AI-assisted development has changed how software gets built. What used to take a week of development can now take a day. Prototypes that used to require a senior engineer can be scaffolded by someone with moderate coding experience using the right AI tools. This is genuinely useful — but it comes with a risk that is not always obvious until a project is in production.
Code that works in a prototype often has structural problems that are invisible at small scale and expensive at real scale. The patterns that make AI-generated code fast to produce are often the same patterns that make it fragile to maintain.
What AI Code Generation Gets Right
AI tools are genuinely good at producing code for well-defined, standard patterns. REST API endpoints following common conventions, database queries for standard CRUD operations, UI components that follow established frameworks, utility functions with clear inputs and outputs — these are areas where AI-generated code is often production-quality on the first pass.
AI is also good at boilerplate and configuration: setting up project structure, writing repetitive but necessary code, converting one format to another, and implementing patterns it has seen many times in its training data.
What AI Code Generation Gets Wrong
The problems appear at the edges — the situations that are slightly different from the standard pattern.
Error handling: AI-generated code often handles the happy path correctly and handles errors poorly. The most common failure is catching exceptions without actually doing anything useful with them — logging a vague message and continuing, when the correct behaviour is to retry, alert, or fail gracefully. In production, silent error swallowing creates bugs that are very difficult to diagnose.
Edge cases: Code generated from a simple description handles the common case. It rarely handles empty inputs, null values, unexpected data types, or boundary conditions that do not appear in the description. These are exactly the inputs production systems receive.
Security: AI-generated code can introduce SQL injection vulnerabilities, insufficient input validation, overly permissive CORS settings, and logging of sensitive data. These are not usually obvious in a prototype but are serious in production.
Scalability assumptions: Code that works for 10 concurrent users often does not work for 1,000. AI-generated code may use patterns that are fine for prototype load and degrade badly at scale — N+1 database queries, unindexed lookups, blocking operations that should be async.
Review Patterns That Matter Most
When reviewing AI-generated code for production use, prioritise these areas:
- Every error handling path: What happens when an external API call fails? When the database is unavailable? When an input is empty or malformed?
- Input validation at boundaries: Every input from an external source (user input, API response, file content) should be validated before use.
- Authentication and authorisation: Who can call what? Are there endpoints that should require authentication but do not?
- Database queries: Are they using parameterised queries? Are the relevant fields indexed?
- Sensitive data: Is anything sensitive being logged? Stored unencrypted? Returned in API responses that should not include it?
The Right Workflow
The most effective workflow for AI-assisted development treats AI as a first draft, not a final product. Use AI to scaffold the structure and implement the standard patterns quickly. Then review the code with the same rigour you would apply to a junior developer's pull request — because that is approximately the right mental model. It is fast, it is capable, and it needs review before it goes to production.
The goal is not to distrust AI-generated code, but to verify it in the right places. A structured review process that focuses on error handling, security, and edge cases — rather than re-reading every line — produces production-ready code from AI assistance without the overhead of treating every AI output as potentially broken.