Effective gen AI guardrails are required throughout the data, development, deployment and monitoring AI pipelines These guardrails help in mitigating risks such as biased outputs, data privacy breaches, and toxic content generation. Here are some key examples of guardrails that can be implemented in Gen AI pipelines:
- Prompt Engineering - Crafting input prompts to guide the AI model toward generating desirable outputs. These prompts discourage the model from generating toxic or biased outputs and provide context that steers the model in a safer direction.
- LLM as a Judge - Using an LLM to evaluate the output’s compliance with predefined rules and standards. When a violation is detected, the LLM flags the issue or provides recommendations for modification to ensure alignment with the guidelines.
- Toxicity Measurement with Language Filters - Implementing language filters to measure and flag toxicity levels in generated outputs. These filters use pre-trained models or rule-based systems to detect offensive, harmful, or otherwise inappropriate language.
- Bias Detection and Mitigation - Techniques such as adversarial testing, fairness metrics and model retraining to identify and mitigate biases within AI models. This involves detecting biases towards specific populations, genders, or other demographic groups.
- Data Privacy Checks - Checks for sensitive information. This could involve using regular expressions (regex) to identify and redact personal information such as social security numbers, addresses, and other private data. This step allows complying with regulations like GDPR or CCPA and protecting user privacy.
- Hallucination Detection - Using knowledge bases or fact-checking algorithms to compare generated content with verified information, ensuring the AI’s output remains credible and factual. This can be implemented with RAG and vector databases. For example, in a financial use case, the LLM can scan financial documents and get a numeric value for the response.
- Human-in-the-Loop (HITL) Validation - Having human moderators review and validate AI-generated content, especially in high-stakes applications.
- Ethical and Compliance Frameworks - Adopting ethical and compliance frameworks, such as the AI Ethics Guidelines set by organizations like the EU or specific industry standards, to establish a baseline for acceptable AI behavior. These frameworks can be integrated into the AI development process, ensuring that ethical considerations are factored into every stage of the AI pipeline.
Different guardrails can be implemented in different parts of your application pipeline. For example:
- A toxicity filter could filter user input before hitting the model as well as model output. It is the same type of check but in two different places.
- Hallucination detection can be used for the model output
- Data privacy checks could be used before sending a model to redact PII. They could also be used earlier, during data ingestion into vector stores for RAG.
