Exam Certified Generative AI Engineer Associate topic 1 question 60 discussion

Llama Guard: Llama Guard, an openly available model from Meta, is specifically designed for content safety classification. It acts as an LLM-based safeguard, evaluating both user inputs and LLM outputs to identify and classify unsafe content.

A: User reporting is reactive, risks customer dissatisfaction, and doesn’t prevent unsafe outputs, making it unsuitable for production. B: Llama Guard is a strong candidate, but its availability on Databricks’ Foundation Model API is uncertain, and hosting it requires additional setup (e.g., model serving, endpoint configuration), which increases effort compared to C. D: Regex is quick to implement but unreliable for complex toxicity detection, leading to poor safety outcomes in a customer-facing system.

B. Host Llama Guard on Foundation Model API and use it to detect unsafe responses. By enabling Databricks’ built-in Llama Guard directly on your Foundation Model API endpoint, you get out-of-the-box toxicity and safety checks with zero changes to your application code. The guard runs before responses are returned, blocking or redacting unsafe content according to its policy. This approach requires the least effort compared to adding custom detection calls or regex rules, and is far more proactive than relying on user reports.