AI for PHP Developers: Generate, Test, and Review Code

AI can accelerate PHP development in real ways: generating boilerplate, writing initial implementations from a function signature and description, drafting tests, and reviewing code for common issues. The catch is that PHP projects vary significantly—a Laravel application, a WordPress plugin, and a raw PHP API follow very different patterns, and an AI session that does not know which one it is working on will produce generic code that fits none of them.

Giving the model your framework, PHP version, and relevant conventions before asking it to write code is the most reliable way to get useful output. Tools like fenbs keep those project facts outside any single chat so a connected assistant reads the current context at the start of each session rather than starting cold.

AI-assisted PHP code generation

AI handles PHP code generation well for tasks with a clear pattern: form handlers, REST API controllers, database query methods, middleware functions, and service classes. Give it the method signature, the input and output shape, and any relevant framework conventions, and ask for an initial implementation.

The generated code is a starting point, not a finished deliverable. Review it before using it: check that type declarations are correct for your PHP version, that exceptions are thrown and caught consistently with the rest of the codebase, and that any database calls use your project's query builder or ORM correctly rather than raw queries where they should not be used.

One practical approach is to write the docblock for a function—with the input types, output type, and expected behavior—and ask AI to generate the implementation. You review the logic. The PHP documentation↗ (opens in a new tab) is the definitive source for built-in function behavior when you need to verify what the generated code actually does.

Framework context: Laravel, Symfony, WordPress

The difference between good AI output and generic AI output in PHP work is usually the framework context you provide. Laravel, Symfony, and WordPress each have specific idioms, naming conventions, and preferred approaches. AI trained on general PHP code may suggest patterns that work but are not idiomatic for your framework.

For Laravel: tell the model which major version you are using, whether you are using Eloquent or Query Builder for the data you are working with, and how your application handles authentication and authorization in the relevant part of the codebase. For Symfony: specify the component, the PHP version, and any relevant service definitions. For WordPress: clarify whether you are writing a plugin, a theme function, or a block, and mention any hook priorities or naming conventions in use.

A short context block at the start of a prompt—"This is a Laravel 11 project using Eloquent and Sanctum for API auth"—changes the output significantly. Without it, AI will produce something that looks right but requires more editing than starting from your own pattern.

Writing and running PHP tests with AI

PHP has strong testing tooling, and AI can help you use it faster. PHPUnit and Pest are the main frameworks; AI knows both well. After writing an implementation, ask AI to generate tests covering the happy path, the common failure modes, and any edge cases the docblock implies.

Review the generated tests before running them. Check that assertions test actual behavior rather than just that the method ran. A test that calls a method and asserts the return is not null is not much of a test. Check that mocked dependencies are set up to reflect real behavior, and that database interaction tests use transactions or fixtures rather than writing to your development database.

Integration tests and feature tests are where AI-generated PHP tests most commonly need editing. The test often assumes a fresh application state that may not match your setup. Make the test setup explicit and verify that it can run in isolation.

Code review and security checks

AI can review PHP code for common security issues: SQL injection through unparameterized queries, XSS from unsanitized output, CSRF protection gaps, and insecure file handling. This is not a substitute for a proper security review, but it is useful as a first pass before code review.

When asking AI to review for security, be specific. "Review this code for SQL injection and XSS vulnerabilities" produces more useful output than "review this code." Name the security concerns you are most likely to encounter for the type of code you are working on.

The OWASP PHP Security Cheat Sheet↗ (opens in a new tab) is a good primary reference for PHP-specific security practices. Use it to verify that AI-generated security advice matches current recommendations.

Learning from defects and past mistakes

Every PHP developer accumulates knowledge about the mistakes that cost the most time: the N+1 query that appeared in production, the session handling bug that took a day to trace, the type coercion issue that only surfaced with specific inputs. These lessons are valuable—but they only help if they are recorded somewhere they can be read before similar work begins.

An AI session does not inherit your project's history of mistakes. It will make the same N+1 query suggestion that caused a problem last quarter unless you tell it not to. Writing those lessons down in a shared record and giving that record to the next AI session changes what gets generated.

The AI coding agent benchmarks guide covers how different AI coding tools compare on real-world tasks, which is useful background when choosing which tool to use for PHP work.

How to do this with fenbs

Keep your PHP project's context, conventions, and defect lessons in fenbs so every AI session starts from a relevant and accurate picture.

1. Create a free account. A personal board is free with no credit card required.
2. Add a project for your PHP application. Include the framework, PHP version, and current development goal.
3. Write your project's conventions as Decisions and rules: ORM versus raw SQL boundaries, error handling patterns, test setup requirements, and any security rules.
4. When a defect teaches a lesson—an N+1 issue, a security gap, a type coercion problem—record it as a lesson. A connected assistant reads lessons before similar work begins.
5. Connect a supported AI coding tool over MCP from Integrations. Start each session by asking the assistant to read your project context.