The original title is "AI Metrics Baseline: Prove Your Feature Works Before Scaling It"

Short summary

An AI metrics baseline is a small set of before-and-after measurements that proves whether an AI feature is improving or just getting more expensive. Rather than relying on opinion, teams should track five categories: cost (per request, per successful task, retries), quality (groundedness, accuracy, precision by use case), reliability (success rate, errors, latency), and adoption (repeat usage, acceptance rate, trust). Start with one or two metrics per category and use deterministic checks whenever possible instead of model-as-judge scoring.

• •Establish baseline metrics before scaling AI features to measure actual impact vs. opinion
• •Track five metric categories: cost, quality, reliability, adoption, and business impact
• •Use deterministic checks (schema validation, test pass/fail) over model-as-judge scoring

Generated with AI, which can make mistakes.