Building an Efficient Content Moderation System with LLMs and Unified API Gateways

In today's UGC (User Generated Content) ecosystem, content moderation is not just a compliance baseline, but a lifeline for platform survival. For independent developers and small teams facing massive amounts of varied violating content—ranging from pornography and violence to advertisement diversion and even new variant slang—traditional keyword filtering has long been inadequate. Furthermore, building custom models faces the "three big mountains" of difficult data labeling, high computing costs, and slow model iteration.

As an AI application architect, I have witnessed too many small teams being dragged down by violating content during business growth spurts because they neglected risk control architecture design. This article uses a typical "second-hand trading platform" scenario to explore how to utilize existing LLM (Large Language Model) capabilities combined with a unified AI API gateway to rapidly build a highly available, low-cost, and easy-to-maintain content moderation system.

I. Business Pain Points: Why Traditional Solutions Fail?

Suppose we are developing a campus second-hand trading app called "Idle Transfer". Initially, to save costs, we might only deploy a simple keyword blocking library. However, as the user base grows, the following pain points will quickly surface:

1. High False Positive Rate, Poor Experience: A user posts "Mountain bike transfer", which the keyword library misjudges as pornographic due to character segmentation, resulting in a ban and user churn.
2. Difficulty Identifying Variant Content: Violators use variant terms like "WeiXin", "VX", "Add me to see pics", or even homophones and pictographic symbols to bypass regex matching. Simple string matching is helpless against this.
3. Runaway Maintenance Costs: To cope with new variants, the dev team has to manually update thousands of regex rules daily, falling into an infinite "whack-a-mole" loop with no time for core business development.
4. Lack of Context Understanding: A user comments "This thing sucks, it's just trash". Traditional solutions struggle to distinguish between malicious abuse and objective complaining about product quality.

For small teams, the core resources are "development time" and "funds". We need a solution that understands semantics, iterates quickly, and doesn't require maintaining underlying model infrastructure.

II. Architecture Design: Layered Defense and Intelligent Routing

Addressing these pain points, we designed a "funnel-style" layered moderation architecture. The core philosophy is: Never waste model compute on what rules can solve; never call a large model for what a small model can solve.

#### 1. Overall Architecture Diagram

The system is divided into three layers:

• L1: Local Rule Layer (Millisecond response, zero cost)
• Utilizes an in-memory database for high-frequency blacklist matching.