All posts
-
Constitutional AI Explained: How Principle-Based Training Builds Safer Models
Constitutional AI replaces human harm labels with a written set of principles and AI self-critique. Here is how the method works, where it sits in your
-
LLM Guardrails Explained: What They Are and How to Implement Them
A practitioner's guide to LLM guardrails — the five rail types, what each one actually catches, where each is bypassed, and how to wire a stack that fails
-
MCP Tool Poisoning: The Guardrail Layer Most Teams Are Missing
MCP makes every server an injection surface in your LLM app. Tool poisoning, rug-pulls, and the lethal trifecta are live. Here is what to actually defend.
-
G4-MeroMero-31B: Abliteration Drops Refusal Rate 99% to 15%
A new uncensored fine-tune of Gemma 4 31B achieves a 15/100 refusal rate via Arbitrary-Rank Ablation on attention output projections — KL divergence 0.
-
AI Moderation Tools for LLMs: What Works and What Gets Bypassed
A practitioner's comparison of AI moderation tools — AWS Bedrock Guardrails, Azure AI Content Safety, Lakera Guard, NeMo Guardrails, and Llama Guard —
-
LLM Alignment Evaluation: Why Benchmarks Don't Predict Safety
Practitioners rely on alignment benchmarks that miss the attack surface that matters: agentic tasks, implicit harm, and low-resource languages.
-
AI Safety Tools: A Guide to Guardrails, Filters, and Defenses
A practitioner's breakdown of the leading AI safety tools — NeMo Guardrails, LLM Guard, Llama Guard, and managed platforms — with benchmark data, known
-
KV Cache Compression Is Now an Alignment Problem
A new preprint argues that compressing KV cache during RL rollouts silently biases the policy you ship. For teams treating RLHF as a defensive control
-
ChatGPT Safety: How OpenAI's Guardrails Work and Fail
ChatGPT safety explained: how RLHF, Rule-Based Rewards, safe-completions, and the Moderation API work, plus the jailbreaks that defeat each layer.
-
LLM Alignment: What It Does, Where It Breaks, How to Deploy
LLM alignment trains models to internalize safety constraints — but every technique has documented bypass paths.
-
LLM Guardrails: Comparing Tools and Implementation Patterns
A practical comparison of LLM guardrail implementations — classifiers, rule engines, LLM judges — with empirical bypass rates and deployment patterns that
-
LLM Guardrails: Architecture, Bypasses, and What to Deploy
LLM guardrails are the control layer between a language model and the real world. This guide covers how they work, how they fail under adversarial
-
LLM Safety: What It Actually Means and How to Build It
LLM safety spans alignment training, inference-time guardrails, and external filters — each with known failure modes.
-
LLM Security Tools: A Practical Guide to the Current Stack
A working guide to LLM security tools for 2026 — covering red-teaming frameworks, runtime guardrails, and observability layers, with honest notes on what
-
Model Alignment: What It Is, How It Works, and Where It Fails
Model alignment trains AI systems to follow human intent rather than optimize for proxy metrics. Here's what the main techniques actually do, how they're
-
Content Moderation AI Tools: Benchmarks, Bypasses, and Deployment
A practitioner's comparison of leading content moderation AI tools — OpenAI Moderation, Azure AI Content Safety, Llama Guard 4, NeMo Guardrails, and more
-
AI Content Filter: Architecture, Bypasses, and Layered Defense
A practitioner's breakdown of AI content filter approaches — classifier-based, LLM-as-judge, and guard models — with honest coverage of bypass techniques
-
Output Classification: A PII and Secrets Detector for LLM Apps
Most output filters catch the obvious cases and miss the long tail. Here's how to build an output classifier that's actually deployable in production.
-
Content Moderation Tools for LLMs: What Works and Where It Breaks
A practitioner's guide to the leading content moderation tools for LLM applications—OpenAI Moderation API, Llama Guard, Perspective API, and
-
OpenAI's Under-18 Principles: An Engineer Reads the Model Spec
OpenAI's December Model Spec adds Root-level Under-18 Principles that bind the model even against jailbreak framing.
-
AI Content Moderation: How LLM Filters Work and Where They Break
A technical breakdown of AI content moderation for LLM applications — how classifier-based guardrails work, the bypass techniques that defeat them, and
-
OpenAI's Under-18 Principles: What the New Model Spec Does
OpenAI's December 18 Model Spec adds Under-18 Principles, an age-prediction classifier, and real-time moderation across modalities.