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LLM Safety: RLHF, Constitutional AI, Content Filtering, Red Teaming

Introduction

As LLMs are deployed in sensitive applications, safety mechanisms are essential. Models can produce harmful content, leak private information, or be manipulated through prompt injection. This article covers the four layers of LLM safety: training-time alignment through RLHF, runtime constraints with Constitutional AI, automated content filtering, and adversarial testing via red teaming.

RLHF (Reinforcement Learning from Human Feedback)

RLHF trains the model to prefer helpful and harmless responses:

# RLHF training pipeline (simplified)

# Step 1: Supervised fine-tuning on demonstration data

# Step 2: Train a reward model on human preference comparisons

# Training a reward model

reward_training_data = [

{"chosen": "I cannot help with that request.", "rejected": "Sure,…