Guardrails with NeMo and LlamaGuard: full course guide
Companies are terrified of their AI going off-script. Build a pre-processing layer that filters user intent and a post-processing layer that validates AI output before the user sees it.
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What is inside this guide
01Why Guardrails Matter
- Real-World AI Failures from Missing Guardrails
- Input Threats: Prompt Injection, Jailbreaks, Off-Topic Queries
- Output Risks: Hallucinations, Harmful Content, PII Leakage
- Compliance and Legal Implications for Enterprises
- Overview of NeMo Guardrails vs LlamaGuard
02Pre-Processing — Filtering User Intent
- LlamaGuard: Intent Classification Categories
- Scoring User Input for Safety Before Calling the LLM
- NeMo Guardrails: Colang Rail Definitions
- Topic Restriction Rails: Keeping AI On-Script
- Handling Blocked Requests Gracefully in the UI
03Post-Processing — Validating AI Output
- Output Validation: Fact-Checking, PII Detection, Toxicity
- LlamaGuard Output Classification Pipeline
- NeMo Output Rails: Blocking Unsafe Responses
- Latency Impact of Dual-Layer Guardrails and Mitigation
- Capstone: End-to-End Guardrail Pipeline for a Customer Support Bot
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