webapp-ai-llm

| | Input Rail | A guardrail that intercepts and validates user input before it reaches the LLM, blocking injection attempts and redacting sensitive data | | Output Rail | A guardrail that validates LLM-generated output before it reaches the user, filtering toxic content and enforcing schema compliance | | Colang | NVIDIA's domain-specific language for defining conversational guardrail flows, with Python-like syntax for specifying user intent patterns and bot responses | | PII Redaction | The process of detecting and masking personally identifiable information (names, emails, SSNs) in text before processing | | Content Policy | A configuration file defining which topics, patterns, and content categories are allowed or blocked by the guardrail system | | Self-Check Rail | A NeMo Guardrails technique where the LLM itself evaluates whether its input or output violates defined policies | | Hallucination Detection | Output validation that checks whether the LLM response is grounded in the provided context, flagging fabricated claims |

Tools & Systems

• NVIDIA NeMo Guardrails: Open-source toolkit for adding programmable input, dialog, and output rails to LLM applications using Colang flow definitions and YAML configuration
• Guardrails AI: Python framework for structured output validation with a hub of pre-built validators for PII, toxicity, JSON schema compliance, and more
• Microsoft Presidio: Open-source PII detection and anonymization engine supporting 30+ entity types with configurable NLP backends
• Colang 2.0: Event-driven interaction modeling language for defining guardrail flows with Python-like syntax, supporting multi-turn dialog control
• OpenAI Guardrails Python: OpenAI's client-side guardrails library for prompt injection detection and content policy enforcement