Lilly Tech Systems
Learn LLM Output Sanitization
Master the techniques for filtering harmful content, preventing PII leakage, blocking code injection in AI outputs, and implementing guardrails frameworks to ensure your LLM applications produce safe, compliant responses.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
Why output sanitization matters. Understand the risks of unsanitized LLM outputs and the compliance landscape.
2. Output Risks
Catalog the dangers: harmful content generation, PII leakage, code injection, hallucinated data, and bias amplification.
3. Filtering Techniques
Implement regex filters, keyword blocklists, ML-based classifiers, and semantic similarity checks for output safety.
4. Content Moderation
Build moderation pipelines using OpenAI Moderation API, Perspective API, and custom toxicity classifiers.
5. Code Safety
Sanitize code outputs to prevent injection attacks, detect malicious patterns, and sandbox execution environments.
6. Best Practices
Production guardrails with NeMo Guardrails, Guardrails AI, layered filtering, monitoring, and incident response.
What You'll Learn
By the end of this course, you'll be able to:
Identify Output Risks
Recognize harmful content, PII leaks, code injection vectors, and other dangers in raw LLM outputs before they reach users.
Build Filter Pipelines
Implement multi-layer filtering using regex, ML classifiers, and semantic analysis to catch unsafe content reliably.
Moderate Content at Scale
Deploy content moderation APIs and custom classifiers that handle millions of outputs with low latency.
Deploy Guardrails Frameworks
Use NeMo Guardrails and Guardrails AI to create production-grade safety systems with configurable policies.