Observability

Plain-English explanation

AI observability covers prompts, retrieved context, tool calls, latency, cost, errors, model versions, user feedback, and eval results.

Why it matters

Observability matters because it affects how AI systems are designed, evaluated, priced, or trusted. Knowing the term helps you ask better questions and avoid vague implementation decisions.

• Ask how it changes quality, cost, speed, or safety.
• Look for concrete examples in the workflow you are building.
• Document the tradeoff before choosing a tool or architecture.