How Enterprise h2oGPTe Guardrails Keep LLMs Safe
- Generative AI
- Enterprise h2oGPTe
This lesson covers how Enterprise h2oGPTe enforces guardrails to keep large language model systems safe and compliant in production. It walks through guardrails applied at ingestion, prompt submission, and response generation, including built in and custom toxic topic classifications. It also covers personally identifiable information detection, combining regex pattern matching, the open source Presidio library, and a fine tuned BERT based model from H2O, along with prompt guard detection for adversarial jailbreak attempts. Every guardrail violation is logged for audit and compliance review.
Key takeaways
- How Enterprise h2oGPTe applies guardrails at three stages: content ingestion, prompt submission, and response generation
- What built in and custom guardrails do to keep an AI assistant restricted to approved topics
- How personally identifiable information detection combines regex patterns, Presidio, and a fine tuned BERT based model
- What prompt guard detects, adversarial jailbreak patterns like instructions to ignore prior guidance
- Why every guardrail violation is logged, to support compliance investigations and usage audits
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