Content Moderation for AI Applications: A Developer's Guide

Every LLM-powered application needs content moderation. Not just for compliance — for safety, for user trust, and for protecting your model from adversarial inputs.

In this guide, we'll cover the two sides of AI content moderation: filtering what users send to your LLM, and filtering what your LLM sends back to users.

The two filters every AI app needs

1. Input filtering

Before user input reaches your LLM, you need to check for:

• Harmful content: Hate speech, harassment, threats, sexual content
• Prompt injection: Attempts to override system prompts or jailbreak the model
• Illicit requests: Instructions for illegal activities, drug manufacturing, etc.
• PII leakage: Users accidentally sharing personal information

2. Output filtering

Before model output reaches your users, you need to check for:

• Harmful generated content: The model producing hate speech, violence, etc.
• PII generation: The model accidentally generating real or fabricated personal data
• Hallucinated dangerous information: Medical advice, legal counsel, financial recommendations
• Policy violations: Content that violates your application's terms of service

Implementation patterns

Pattern 1: Sequential filtering (recommended for most apps)

User Input -> Input Moderation -> { if flagged -> reject } -> LLM
LLM Output -> Output Moderation -> { if flagged -> fallback response } -> User

Pattern 2: Parallel filtering (for low-latency requirements)

User Input -> Input Moderation + LLM (in parallel)
-> Wait for moderation result
-> If flagged -> discard LLM response & show error
-> If not flagged -> show LLM response after output moderation

Defense in depth with multiple providers

Relying on a single moderation provider creates a single point of failure and blind spots. A layered approach is more robust:

• Use OpenAI Moderation for general harmful content detection
• Use Llama Guard (self-hosted) for zero-cost, zero-data-leakage filtering of sensitive inputs
• Use LLM-as-classifier with GPT-4o or Claude for custom policy enforcement with rich context understanding

With OpenModeration, this layered approach is a single API call. Set up rules that route content through multiple providers and aggregate results for a final decision.

Best practices

1. Always moderate both input and output — they catch different types of risk
2. Use context for better accuracy — pass conversation history and author info to improve moderation decisions
3. Set appropriate thresholds — lower thresholds for high-risk categories (minors, self-harm), higher for low-risk (spam)
4. Log everything for audit — you need to prove your moderation works, especially for regulated industries
5. Test with adversarial inputs — regularly test your moderation pipeline with known jailbreak prompts