How to Evaluate LLM Outputs in Production Using Rubrics and Guardrails

Developers building production AI systems cannot rely on HTTP status codes alone to confirm output quality, as a successful server response may still contain hallucinated or harmful content. The Air Canada chatbot case illustrates this risk: the system returned valid responses while fabricating a bereavement discount policy, ultimately resulting in a tribunal ruling against the airline. A practical evaluation framework combines system prompts that define quality criteria, scored rubrics assessed by a second LLM acting as a judge, and runtime guardrails that block or flag unsafe outputs. This LLM-as-a-judge approach scores responses on dimensions such as factual accuracy, relevance, and harmlessness, scaling more efficiently than manual human review. Running evaluations asynchronously, logging all decisions, and updating rubrics via prompt changes allows teams to maintain continuous output quality monitoring in live environments.