The original title is "A Practical Guide to LLM Evaluation: System Prompts, Scored Rubrics, and Runtime Guardrails"

Short summary

A practical guide to evaluating LLM outputs in production by combining system prompts, scored rubrics, and runtime guardrails. The Air Canada chatbot case illustrates how a 200 status code masks fabricated content — operational health must be separated from output quality. The author provides concrete system prompt templates for LLM-as-a-judge evaluation, structured rubrics with mutually exclusive scoring levels, and code examples for flagging low-quality outputs in real time.

• •200 status codes mask qualitative failures — separate operational health from output quality
• •LLM-as-a-judge with structured rubrics scores dimensions like factual accuracy and relevance on defined scales
• •Runtime guardrails flag or filter unsafe outputs alongside every user-facing response

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