Question

A Generative AI Engineer deploys a customer-facing chatbot on a Databricks Model Serving endpoint. Compliance requires that any generated response containing toxic, hateful, or unsafe content must be blocked before it reaches the end user, and the blocking decision must happen on outputs the LLM produces (not just user inputs). The team wants to use Databricks-native tooling with minimal custom code. Which approach best satisfies this requirement?

Accepted Answer

Enable AI Guardrails safety filters on the serving endpoint so that both prompts and model responses are screened, and unsafe outputs are blocked or flagged before returning to the user

Answer

Rely solely on a system prompt instructing the model to never produce harmful content, since well-crafted prompts guarantee safe outputs

Answer

Apply Unity Catalog column masking on the source Delta tables so toxic content is never retrieved during RAG

Answer

Configure inference tables to log all responses and run a nightly batch job that reviews flagged outputs after they have been sent to users