AI-Assisted Coding in Practice: Building a Multi-Tenant POS System with Laravel 12 and Livewire 3

When AI Becomes Your Pair Programmer

A developer built a fully functional multi-tenant POS (Point of Sale) system from scratch using Google's AI coding tools. The tech stack consisted of Laravel 12, Livewire 3, MySQL, and Tailwind CSS. The entire process demonstrated the actual capability boundaries of AI-assisted development today — what it can do, and where human intervention is still needed.

The system ultimately implemented multi-tenant management, role-based access control (admin/cashier), inventory tracking, real-time currency switching, and can be deployed on a VPS or shared hosting. The developer stated that payment gateways, product images, and native mobile support will be added next.

A multi-tenant POS system refers to a single software platform that serves multiple independent merchants simultaneously, where each merchant has their own products, inventory, staff, and sales data, all isolated from one another. Compared to a single-tenant system, it requires a multi-tenant architecture to ensure data doesn't leak across tenants, and supports platform-level management of merchant onboarding, review, and billing. These systems typically target restaurants, retail, and chain stores, with high demands for access control and real-time performance.

Regarding the tech stack, Laravel 12 is the most popular full-stack web framework in the PHP ecosystem, providing ORM, queues, authentication, and other infrastructure; Livewire 3 is its official reactive component library that allows developers to write frontend interactions in PHP without extensive JavaScript; MySQL handles persistent storage; Tailwind CSS provides atomic styling to accelerate UI development. This combination is well-suited for rapidly building small-to-medium-sized backend systems.

The PRD Document: AI Programming's "North Star"

The developer's core methodology is: write the PRD first, then let AI write the code. He created a Markdown file called prd.md that detailed product requirements, tech stack, feature modules, and business rules, then used it as a persistent reference document for the AI.

"The PRD is one of the most important documents when you're programming with AI," he emphasized in the video. This document defines the boundaries of every task the AI executes — like giving a highly capable but context-lacking engineer a detailed requirements specification.

Tip: Before letting AI execute any changes, preview its change plan first. Once you confirm the direction is correct, then commit. This developer insists on "never letting AI execute directly — I want to see what it plans to change first."

The effectiveness of this approach was validated later: when currency management features needed to be added, the AI could understand the existing architecture and generate consistent code, rather than reinventing the wheel each time.

PRD (Product Requirements Document) is a standardized document used in product development to align on requirements, typically containing target users, feature lists, business flows, data rules, and acceptance criteria. In AI programming scenarios, the PRD's role expands from "for humans to read" to "for the model to read": it acts as a persistent system prompt that lets AI reference the same source of truth every time it generates or modifies code, reducing drift caused by "context window" loss.

Compared to traditional verbal requirements or scattered comments, a structured PRD can significantly reduce the probability of AI hallucinations because it explicitly defines the tech stack, module boundaries, and business rules. By placing the PRD in the project root and continuously updating it, the developer is essentially building a searchable "project memory" for the AI — which is one of the more mature best practices in AI-assisted development today.

Multi-Tenant Architecture and Role-Based Access Design

The system's core design is a multi-tenant architecture. Each merchant logs in through a unique business code (e.g., 1001) and enters their own independent admin panel. The permission hierarchy is clearly divided into three levels:

• Platform Admin: Creates and manages merchants, reviews KYC information
• Merchant Admin: Manages products, inventory, team, and cashiers; configures business settings
• Cashier: Can only access the POS checkout interface and sales records

Role boundaries are strictly enforced in the implementation. After logging in, cashiers cannot see admin menus, cannot modify prices or inventory data, and can only perform sales operations. Merchant admins have full business control, including adding and removing team members.

This design ensures the system's security — one merchant's data and operations cannot affect other merchants.

Multi-tenant architecture is a core design pattern for SaaS systems, with three common implementations: shared database with tenant ID fields, separate databases, and separate schemas. The multi-tenant POS system in this article likely uses the shared database approach, automatically filtering data at the query layer through merchant business codes, achieving a balance between cost and isolation.

KYC (Know Your Customer) is a common compliance process in the finance and payments domain, requiring platforms to verify a merchant's identity, business license, and bank account information before onboarding, to prevent money laundering and fraud. In a POS system, platform admin KYC review means the system needs to support document uploads, review status transitions, and rejection reason recording — typically more complex than simple CRUD operations, and a key area requiring human verification after AI-generated code.

Issues Exposed During Testing and Their Fixes

The developer encountered several specific problems during the demo:

Modal flickering when clearing the cart — When clicking to clear the cart, a confirmation dialog repeatedly appeared and disappeared. The developer pointed out this wasn't a responsive layout issue, but a flaw in state management logic.

Sidebar menu not loading immediately after login — A page refresh was needed to see the complete navigation menu, indicating a timing issue between authentication state and menu rendering.

Currency settings in an unreasonable location — Currency configuration was initially placed in a sub-page under merchant settings. The developer believed a better approach would be to make it a standalone setting item for quick access.

What do these problems have in common? They're all issues at the level of UI state management and page logic coordination. AI-generated code is architecturally sound, but edge cases in interaction details require human testing and correction.

The Real Efficiency and Limitations of AI Programming

The developer offered a candid summary: "Five years ago, I wrote all of this by hand, and when I hit bugs I'd go dig through Stack Overflow posts. Now AI really does make the work easier."

But he also demonstrated AI's limitations. While AI can generate a complete project skeleton in minutes — including database migrations, models, controllers, and Blade templates — the following areas still require deep developer involvement:

• Architecture decisions: Multi-tenant implementation approach, permission granularity design
• PRD writing: Translating business requirements into structured documents that AI can understand
• Code review: Human confirmation needed before every AI modification
• Debugging and optimization: Runtime interaction issues need human diagnosis

Testing revealed that for the same project, development time with AI was mainly consumed by the loop of "describe requirements → review code → test → fix." What AI replaced was more the code entry phase, not system design thinking.

Code review is especially critical in AI-assisted development, because large model-generated code may appear runnable on the surface while hiding logic flaws or security risks. The developer's insistence on "previewing change plans before execution" is essentially using human judgment to compensate for AI's insufficient understanding of business context.

UI state management refers to frontend components' response logic to user actions, data changes, and page lifecycle. In frameworks like Livewire, state is often stored in server-side components, with frontend-backend interaction happening via AJAX, making timing issues like "modal flickering" and "delayed menu loading" common. These types of defects typically can't be found through static code analysis — they must be exposed through repeated operations in a real browser, making them one of the hardest areas for current AI programming to automatically cover.

A Practice Direction Worth Trying

The most valuable takeaway from this case isn't any specific technical point, but the "PRD-driven + AI execution + human review" workflow. It solves the biggest pain point in AI programming: context loss. With a persistent PRD document, AI can maintain understanding of the full project picture across every subsequent modification.

The developer plans to add payment gateway integration, product image uploads, GitHub version management, and a merchant subscription billing system in future iterations. These features will further test AI's performance when handling complex business logic.

For developers wanting to try AI programming, the insight from this project is: rather than agonizing over "whether AI can replace programmers," focus your energy on how to write high-quality requirements documents — that's the key variable determining AI output quality.