AI Safety & Guardrails

intermediate ~5 hours safety agents prompts

Master the techniques for building safe, responsible AI applications across providers. This path covers content moderation, jailbreak defense, hallucination reduction, and guardrail patterns — drawing from 6 different sources to show how each provider approaches safety differently.

Cross-provider comparison is central to this path: OpenAI uses a separate moderation endpoint, Anthropic leverages the main model with prompt design, Mistral has its own categorization, and Cohere offers declarative safety modes. Understanding all approaches lets you build defense-in-depth systems.

Steps

  1. Safety best practices openai intermediate

    Comprehensive safety practices for responsible AI deployment — covering moderation, adversarial testing, human oversight, prompt engineering for safety, and production monitoring.

    Start here for a broad overview of responsible AI deployment. OpenAI's safety guide covers the full spectrum from moderation to adversarial testing to human oversight — it sets the mental framework for everything else in this path.

  2. Content Moderation anthropic-platform intermediate

    Using an LLM itself as a safety layer is a powerful pattern. Anthropic's content moderation guide shows how to design Claude-based classifiers with calibrated sensitivity thresholds. Compare with OpenAI's separate moderation endpoint — different architectural choices.

  3. Moderation openai beginner

    OpenAI's free moderation endpoint for detecting harmful content across categories like hate, violence, self-harm, and sexual content in both text and images.

    OpenAI's moderation endpoint is a purpose-built classifier for harmful content categories. It's a free, fast first-pass filter — contrast this with Anthropic's approach of using the main model for moderation, which is more flexible but slower.

  4. Moderation mistral intermediate

    Mistral's moderation API detects harmful content across multiple categories using AI-powered classification for text and conversations

    Mistral offers its own moderation API with different category taxonomies than OpenAI. Understanding how each provider categorizes harmful content helps you design multi-layer safety systems that don't depend on a single vendor.

  5. Mitigate Jailbreaks anthropic-platform intermediate

    The core defense-in-depth playbook for preventing prompt injection and jailbreak attacks. Anthropic's layered approach — combining system prompt hardening, input validation, and output filtering — is the gold standard. No single layer is sufficient.

  6. Reduce Hallucinations anthropic-platform intermediate

    Hallucination mitigation is a safety concern in production systems. These grounding techniques — source documents, direct quoting, uncertainty flagging — apply regardless of which LLM provider you use.

  7. Reduce Prompt Leak anthropic-platform intermediate

    System prompt leakage can expose business logic and safety instructions. These concrete techniques for preventing prompt extraction complement the jailbreak defenses covered earlier — together they form a complete defensive posture.

  8. Handle Streaming Refusals anthropic-platform intermediate

    A production-specific concern most developers miss: what happens when the model starts generating a response and then refuses mid-stream? This guide covers detection strategies and graceful UX recovery patterns.

  9. Guardrails openai-agents intermediate

    Define validators that run alongside the agent loop to enforce business and safety rules.

    Safety in agentic systems is harder — agents take actions, not just generate text. OpenAI's guardrail validators run alongside the agent loop to enforce both safety and business rules. Compare with Anthropic's approach to agent safety constraints.

  10. Safety Modes cohere intermediate

    The safety modes documentation describes how to use default and strict modes in order to exercise additional control over model output.

    Cohere's safety modes offer a simpler, configuration-based approach to safety controls. This contrast with the programmatic guardrails in previous steps shows the spectrum from declarative safety settings to custom validator logic.