What is Metric?Metric kya hai?

🧸 Think of it likeAise samjho

Like the speedometer and fuel gauge in a car: numbers you watch to know how things are going.Gaadi ke speedometer aur fuel gauge jaisa: numbers jinhe dekh kar pata chalta hai sab kaisa chal raha hai.

📘 ExplainedVistaar se

For AI features use layers: model quality (accuracy, faithfulness), user outcomes (time saved, task success), adoption (usage, retention) and business results (revenue, cost per ticket), plus guardrails. Pick metrics before building and avoid relying on one number.AI features ke liye layers use karo: model quality (accuracy, faithfulness), user outcomes (bacha time, task success), adoption (usage, retention) aur business results (revenue, per ticket cost), saath mein guardrails. Banane se pehle metrics chuno aur ek number pe nirbhar mat raho.

💡 Real exampleAsli example

Edit distance between AI draft and final text, a measure of draft quality.AI draft aur final text ke beech edit distance, draft quality ka naap.

📖 Learn it step by stepStep by step seekho

• Metrics and A/B testing for AI featuresAI features ke liye metrics aur A/B testing
• Writing an AI product spec (PRD) with an eval planEval plan ke saath AI product spec (PRD) likhna

🔗 Related termsJude hue shabd

• Guardrail metricA metric that must not get worse while you improve the main metric.Aisa metric jo main metric sudhaarte waqt bigadna nahi chahiye.
• EvalsA repeatable set of test cases and scoring rules that measures whether an AI system works well.Test cases aur scoring rules ka dohraaya ja sakne wala set jo naapta hai AI system acche se kaam karta hai ya nahi.
• A/B testRandomly showing two versions to different users to see which one really works better.Do versions ko randomly alag users ko dikhana, yeh dekhne ke liye ki kaunsa sach mein behtar hai.